citing_id stringlengths 9 16 | cited_id stringlengths 9 16 | section_title stringlengths 0 2.25k | citation stringlengths 52 442 | text_before_citation list | text_after_citation list | keywords list | citation_intent stringclasses 3
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1803.04848 | 1501.07418 | INTRODUCTION | Although the robust approach is computationally efficient when the uncertainty set is state-wise independent, compact and convex, it can lead to overly conservative results #REFR . | [
"A strategy that maximizes the accumulated expected reward is then considered as optimal and can be learned from sampling.",
"However, besides the uncertainty that results from stochasticity of the environment, model parameters are often estimated from noisy data or can change during testing #OTHEREFR Roy et al.,... | [
"For example, consider a business scenario where an agent's goal is to make as much money as possible.",
"It can either create a startup which may make a fortune but may also result in bankruptcy.",
"Alternatively, it can choose to live off school teaching and have almost no risk but low reward.",
"By choosin... | [
"robust approach"
] | background | {
"title": "Soft-Robust Actor-Critic Policy-Gradient",
"abstract": "Robust Reinforcement Learning aims to derive an optimal behavior that accounts for model uncertainty in dynamical systems. However, previous studies have shown that by considering the worst case scenario, robust policies can be overly conservative.... | {
"title": "Distributionally Robust Counterpart in Markov Decision Processes",
"abstract": "This technical note studies Markov decision processes under parameter uncertainty. We adapt the distributionally robust optimization framework, assume that the uncertain parameters are random variables following an unknown d... |
1803.04848 | 1501.07418 | RELATED WORK | These #REFR in which the optimal strategy maximizes the expected reward under the most adversarial distribution over the uncertainty set. | [
"Our work solves the problem of conservativeness encountered in robust MDPs by incorporating a variational form of distributional robustness.",
"The SR-AC algorithm combines scalability to large scale state-spaces and online estimation of the optimal policy in an actor-critic algorithm. Table 1 compares our propo... | [
"For finite and known MDPs, under some structural assumptions on the considered set of distributions, this max-min problem reduces to classical robust MDPs and can be solved efficiently by dynamic programming [Puterman, 2009] .",
"However, besides becoming untracktable under largesized MDPs, these methods use an ... | [
"optimal strategy",
"adversarial distribution"
] | background | {
"title": "Soft-Robust Actor-Critic Policy-Gradient",
"abstract": "Robust Reinforcement Learning aims to derive an optimal behavior that accounts for model uncertainty in dynamical systems. However, previous studies have shown that by considering the worst case scenario, robust policies can be overly conservative.... | {
"title": "Distributionally Robust Counterpart in Markov Decision Processes",
"abstract": "This technical note studies Markov decision processes under parameter uncertainty. We adapt the distributionally robust optimization framework, assume that the uncertain parameters are random variables following an unknown d... |
1906.05988 | 1501.07418 | In Section 3, we formulate the DR Bellman equation and show that the value function is convex, when the ambiguity set is characterized by moments as in #REFR , and introduce several examples of moment-based ambiguity set. | [
"The state then makes a transition according to p and DM's production decision, and the DM receives a reward according to how much demand he/she is able to satisfy, or pays a stocking cost.",
"Assuming a family of distributions of unknown climate, the DM aims to maximize the worst-case revenue given the nature be... | [
"In Section 4, we present an approximation algorithm for DR-POMDP for infinite-horizon case by using a DR variant of the heuristic value search iteration (HVSI) algorithm.",
"Numerical studies are presented in Section 5 to compare DR-POMDP with",
"POMDP, and to demonstrate properties of DR-POMDP solutions based... | [
"moment-based ambiguity"
] | background | {
"title": "Distributionally Robust Partially Observable Markov Decision Process with Moment-based Ambiguity",
"abstract": "We consider a distributionally robust (DR) formulation of partially observable Markov decision process (POMDP), where the transition probabilities and observation probabilities are random and ... | {
"title": "Distributionally Robust Counterpart in Markov Decision Processes",
"abstract": "This technical note studies Markov decision processes under parameter uncertainty. We adapt the distributionally robust optimization framework, assume that the uncertain parameters are random variables following an unknown d... | |
1712.02228 | 1406.7611 | Introduction | These indicators were developed because evidences have been published that this data is -similar to bibliometric data -field-and time-dependent (see, e.g., #REFR . | [
"(3) The publication of the altmetrics manifesto by #OTHEREFR gave this new area in scientometrics a name and thus a focal point.",
"Today, many publishers add altmetrics to papers in their collections (e.g., Wiley",
"and Springer) #OTHEREFR .",
"Altmetrics are also recommended by Snowball Metrics #OTHEREFR f... | [
"Obviously, some fields are more relevant to a broader audience or general public than others #OTHEREFR .",
"and #OTHEREFR introduced the mean discipline normalized reader score (MDNRS) and the mean normalized reader score (MNRS) based on",
"Mendeley data (see also #OTHEREFR .",
"#OTHEREFR propose the Twitter... | [
"indicators",
"data"
] | method | {
"title": "Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data",
"abstract": "Recently, two new indicators (Equalized Mean-based Normalized Proportion Cited, EMNPC, and Mean-based Normalized Proportion Cited, MNPC) were proposed which are intended f... | {
"title": "Validity of altmetrics data for measuring societal impact: A study using data from Altmetric and F1000Prime",
"abstract": "Can altmetric data be validly used for the measurement of societal impact? The current study seeks to answer this question with a comprehensive dataset (about 100,000 records) from ... |
1803.08423 | 1209.1730 | It is known #REFR that G admits two edge-Kempe inequivalent colorings c 1 and c 2 . | [
"The degree of the covering p constructed explicitly in Lemma 4 is precisely d − 1.",
"Note that we pass to a further cover twice when relying on Lemma 3 and the covering degree increases by a factor of β(d − 1) each time.",
"As explained in Remark 2 no further covers are necessery for the proof. This establish... | [
"These are illustrated in the bottom row of Figure 1 .",
"The colors 1, 2 and 3 correspond to blue, red and black, respectively.",
"The required graph covering G and edge-Kempe switches are described in the top row of Figure 1 .",
"These are performed along the bold cycles and indicated by the sign.",
"The... | [
"two edge-Kempe inequivalent"
] | background | {
"title": "Edge Kempe equivalence of regular graph covers",
"abstract": "Abstract. Let G be a finite d-regular graph with a legal edge coloring. An edge Kempe switch is a new legal edge coloring of G obtained by switching the two colors along some bi-chromatic cycle. We prove that any other edge coloring can be ob... | {
"title": "Counting edge-Kempe-equivalence classes for 3-edge-colored cubic graphs",
"abstract": "Two edge colorings of a graph are edge-Kempe equivalent if one can be obtained from the other by a series of edge-Kempe switches. This work gives some results for the number of edge-Kempe equivalence classes for cubic... | |
1702.08166 | 1610.05507 | Related work | Work #REFR used a different analysis and showed a global linear convergence rate in iterate point error, i.e., x k − x * . | [
"Work #OTHEREFR is the first study that establishes a global linear convergence rate for the PIAG method in function value error, i.e., Φ(x k ) − Φ(x * ), where x * denotes the minimizer point of Φ(x)."
] | [
"The authors of #OTHEREFR combined the results presented in #OTHEREFR and #OTHEREFR and provided a stronger linear convergence rate for the PIAG method in the recent paper #OTHEREFR .",
"However, all these mentioned works are built on the strongly convex assumption, which is actually not satisfied by many applica... | [
"global linear convergence"
] | method | {
"title": "Linear Convergence of the Proximal Incremental Aggregated Gradient Method under Quadratic Growth Condition",
"abstract": "Under the strongly convex assumption, several recent works studied the global linear convergence rate of the proximal incremental aggregated gradient (PIAG) method for minimizing the... | {
"title": "Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server",
"abstract": "This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems. The algorithm ... |
1702.08166 | 1610.05507 | Proof of Lemma 2 | The second part is a standard argument, which is different from the optimality condition based method adopted in the proof of Theorem in #REFR . Part 1. | [
"We divide the proof into two parts.",
"The first part can be found from the proof of Theorem 1 in [1]; we include it here for completion."
] | [
"Since each component function f n (x) is convex with L n -continuous gradient, we have the following upper bound estimations:",
"Summing (15) over all components functions and using the expression of g k , we obtain",
"The last term of the inequality above can be upper-bounded using Jensen's inequality as foll... | [
"optimality condition",
"based method"
] | method | {
"title": "Linear Convergence of the Proximal Incremental Aggregated Gradient Method under Quadratic Growth Condition",
"abstract": "Under the strongly convex assumption, several recent works studied the global linear convergence rate of the proximal incremental aggregated gradient (PIAG) method for minimizing the... | {
"title": "Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server",
"abstract": "This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems. The algorithm ... |
1807.00110 | 1610.05507 | Introduction | We note that the approach in #REFR is essentially a primal algorithm that allows for one proximal term (and hence one constrained set). | [
"(This largely rules out primal-only methods since they usually allow just one proximal term.) Hence, the algorithm would be able to allow for constrained optimization, where the feasible region is the intersection of several sets.",
"(6) able to allow for time-varying graphs in the sense of #OTHEREFR (to be robu... | [
"Due to technical difficulties (see Remark 4.3), a dual or primal-dual method seems necessary to handle the case of more than one constrained set.",
"Algorithms derived from the primal dual algorithm #OTHEREFR , like #OTHEREFR , are very much different from what we study in this paper.",
"The most notable diffe... | [
"primal algorithm"
] | background | {
"title": "Linear and sublinear convergence rates for a subdifferentiable distributed deterministic asynchronous Dykstra's algorithm",
"abstract": "Abstract. In [Pan18a, Pan18b], we designed a distributed deterministic asynchronous algorithm for minimizing the sum of subdifferentiable and proximable functions and ... | {
"title": "Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server",
"abstract": "This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems. The algorithm ... |
1806.09429 | 1610.05507 | Comparison of the results with the literature | In the case of uniformly bounded delays, the derived link between epoch and time sequence enables us to compare our rates in the strongly convex case (Theorem 3.1) with the ones obtained for PIAG #REFR 27, 28] . | [
"This simple but powerful remark is one of the main technical contributions of this paper.",
"In order to get comparisons with the literature, the following result provides explicit bounds on our epoch sequence for our framework with two different kind of bounds on delays uniformly in time.",
"The proof of this... | [
"To simply the comparison, let us consider the case where all the workers share the same strong convexity and smoothness constants µ and L.",
"The first thing to notice is that the admissible stepsize for PIAG depend on the delays uniform upper bound d which is practically concerning, while the usual proximal gra... | [
"uniformly bounded delays"
] | result | {
"title": "A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm",
"abstract": "We develop and analyze an asynchronous algorithm for distributed convex optimization when the objective writes a sum of smooth functions, local to each worker, and a non-smooth function. Unlike many existing methods, our di... | {
"title": "Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server",
"abstract": "This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems. The algorithm ... |
1611.08022 | 1610.05507 | Assumption 2.2. (Strong Convexity) | Before presenting the main result of this work, we introduce the following lemma, which was presented in #REFR , in a slightly different form. | [
"3. Main Result.",
"In this section, we characterize the global linear convergence rate of the PIAG algorithm. Let",
"denote the suboptimality in the objective value at iteration k.",
"The paper #OTHEREFR presented two lemmas regarding the evolution of F k and ||d k || 2 .",
"In particular, the first lemma ... | [
"This lemma shows linear convergence rate for a nonnegative sequence Z k that satisfies a contraction relation perturbed by shocks (represented by Y k in the lemma).",
"Lemma 3.3.",
"[1, Lemma 1] Let {Z k } and {Y k } be a sequence of non-negative real numbers satisfying",
"for any k ≥ 0 for some constants α ... | [
"following lemma"
] | background | {
"title": "A Stronger Convergence Result on the Proximal Incremental Aggregated Gradient Method",
"abstract": "Abstract. We study the convergence rate of the proximal incremental aggregated gradient (PIAG) method for minimizing the sum of a large number of smooth component functions (where the sum is strongly conv... | {
"title": "Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server",
"abstract": "This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems. The algorithm ... |
1711.01136 | 1610.05507 | Key Lemmas and Main Results | First of all, we introduce a key result, which was given in #REFR . Lemma 1. | [
"Throughout this section, we remind the reader that for simplicity we consider the sequence {x k } generated by the PLIAG method with α k ≡ α.",
"All the obtained results and the proofs are also valid for the PLIAG method with different α k ."
] | [
"Assume that the nonnegative sequences {V k } and {w k } satisfy",
"for some real numbers a ∈ (0, 1), b ≥ 0, c ≥ 0, and some nonnegative integer k 0 .",
"Assume also that w k = 0 for k < 0, and the following holds:",
"In addition, we need another crucial result, which can be viewed as a generalization of the ... | [
"Lemma"
] | background | {
"title": "Proximal-Like Incremental Aggregated Gradient Method with Linear Convergence under Bregman Distance Growth Conditions",
"abstract": "We introduce a unified algorithmic framework, called proximal-like incremental aggregated gradient (PLIAG) method, for minimizing the sum of smooth convex component functi... | {
"title": "Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server",
"abstract": "This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems. The algorithm ... |
1810.10328 | 1606.06511 | Time Complexity | Similarly to other well-established machine learning algorithms which share this bottleneck, one could make use of approximations that would trade off accuracy for computational expenses #REFR . | [
"The algorithm requires the computation of a similarity matrix which would require O(N 2 ), where N is the number of data points, and then compute the generalized Laplacian.",
"The bottleneck is computing its inverse which has complexity O(N 3 )."
] | [
"We also note that the per iteration complexity scales linearly in N , due to the normalization step."
] | [
"well-established machine learning",
"algorithms"
] | background | {
"title": "LABEL PROPAGATION FOR LEARNING WITH LABEL PROPORTIONS",
"abstract": "Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual... | {
"title": "Literature survey on low rank approximation of matrices",
"abstract": "Low rank approximation of matrices has been well studied in literature. Singular value decomposition, QR decomposition with column pivoting, rank revealing QR factorization (RRQR), Interpolative decomposition etc are classical determ... |
1802.08901 | 1606.06511 | Hermitian Space -Dynamic Mode Decomposition with control | The use of E-SVD reduces the complexity to O(mnr) ( #REFR ]) by computing only the first r singular values and vectors. | [
"Because the solar cycle lasts over a decade, this requires a large data set of more than (m ≈) 400,000 snapshots with a 0.25 hr resolution.",
"A 5 degree grid resolution in TIE-GCM results in a state vector size of (n ≈) 75,000 with a 2.5 degree grid resolution resulting in n ≈ 300, 000.",
"Large data has moti... | [
"HS-DMDc reduces the computation of the psuedoinverse ( † ) to the Hermitian space by performing an eigendecomposition of the correlation matrix,",
"n×n , reducing the full rank complexity to O(nn 2 ).",
"The complexity can be reduced to O(n 2 r) using an economy EigenDecomposition (E-ED).",
"In theory, the c... | [
"E-SVD"
] | method | {
"title": "M ar 2 01 8 A quasi-physical dynamic reduced order model for thermospheric mass density via Hermitian Space Dynamic Mode Decomposition",
"abstract": "Thermospheric mass density is a major driver of satellite drag, the largest source of uncertainty in accurately predicting the orbit of satellites in low ... | {
"title": "Literature survey on low rank approximation of matrices",
"abstract": "Low rank approximation of matrices has been well studied in literature. Singular value decomposition, QR decomposition with column pivoting, rank revealing QR factorization (RRQR), Interpolative decomposition etc are classical determ... |
2004.03623 | 1704.00648 | Experiments | For relaxed bernoulli in Q O , we start with the temperature of 1.0 with an annealing rate of 3 × 10 −5 (following the details in #REFR ). | [
"For ImageNet, φ(x) is a ResNet18 model (a conv layer followed by four residual blocks).",
"For all datasets, Q A and Q O have a single conv layer each.",
"For classification, we start from φ(x), and add a fully-connected layer with 512 hidden units and a final fully-connected layer as classifier. More details ... | [
"For training the classifier, all methods use stochastic gradient descent (SGD) with momentum with a minibatch size of 128.",
"Initial learning rate is 1 × 10 −2 and we reduce it by a factor of 10 every 30 epochs.",
"All experiments are trained for 90 epochs for CIFAR100 and Indoor67, 5 epochs for Places205, an... | [
"details",
"relaxed bernoulli"
] | method | {
"title": "PatchVAE: Learning Local Latent Codes for Recognition",
"abstract": "Unsupervised representation learning holds the promise of exploiting large amounts of unlabeled data to learn general representations. A promising technique for unsupervised learning is the framework of Variational Auto-encoders (VAEs)... | {
"title": "Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations",
"abstract": "We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entro... |
1811.12817 | 1704.00648 | Loss | Thereby, the z (s) = F (s) (x) are defined using the learned feature extractor blocks E (s) , and p(x, z #REFR , . . . | [
"We are now ready to define the loss, which is a generalization of the discrete logistic mixture loss introduced in #OTHEREFR . Recall from Sec.",
"3.1 that our goal is to model the true joint distribution of x and the representations z (s) , i.e., p(x, z #OTHEREFR , . . .",
", z (s) ) as accurately as possible... | [
", z (s) ) is a product of discretized (conditional) logistic mixture models with parameters defined through the f (s) , which are in turn computed using the learned predictor blocks D (s) . As discussed in Sec.",
"3.1, the expected coding cost incurred by coding x, z #OTHEREFR",
"Note that the loss decomposes ... | [
"learned feature extractor"
] | method | {
"title": "Practical Full Resolution Learned Lossless Image Compression",
"abstract": "We propose the first practical learned lossless image compression system, L3C, and"
} | {
"title": "Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations",
"abstract": "We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entro... |
2001.09417 | 1704.00648 | Image Compression based on DNN | In #REFR , similar to the soft quantization strategy, a soft entropy is designed by summing up the partial assignments to each center instead of counting. | [
"With the quantizer being differentiable, in order to jointly minimize the bitrate and distortion, we also need to make the entropy differentiable.",
"For example, in #OTHEREFR , the quantizer is added with uniform noise.",
"The density function of this relaxed formulation is continuous and can be used as an ap... | [
"In #OTHEREFR , an entropy coding scheme is trained to learn the dependencies among the symbols in the latent representation by using a context model. These methods allow jointly optimizing the R-D function."
] | [
"soft quantization strategy"
] | method | {
"title": "Deep Learning-based Image Compression with Trellis Coded Quantization",
"abstract": "Recently many works attempt to develop image compression models based on deep learning architectures, where the uniform scalar quantizer (SQ) is commonly applied to the feature maps between the encoder and decoder. In t... | {
"title": "Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations",
"abstract": "We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entro... |
2002.10032 | 1704.00648 | INTRODUCTION | In #REFR , a soft-to-hard vector quantization approach was introduced, and a unified framework was developed for image compression. | [
"Deep learning-based image compression #OTHEREFR has shown the potential to outperform standard codecs such as JPEG2000, the H.265/HEVC-based BPG image codec #OTHEREFR , and the new versatile video coding test model (VTM) #OTHEREFR .",
"Learned image compression was first used in #OTHEREFR to compress thumbnail i... | [
"In order to take the spatial variation of image content into account, a contentweighted framework was also introduced in #OTHEREFR , where an importance map for locally adaptive bit rate allocation was employed to handle the spatial variation of image content.",
"A learned channel-wise quantization along with ar... | [
"image compression",
"soft-to-hard vector quantization"
] | method | {
"title": "Generalized Octave Convolutions for Learned Multi-Frequency Image Compression",
"abstract": "Learned image compression has recently shown the potential to outperform all standard codecs. The state-of-the-art ratedistortion performance has been achieved by context-adaptive entropy approaches in which hyp... | {
"title": "Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations",
"abstract": "We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entro... |
2002.01416 | 1803.06893 | 2D Kelvin-Helmholtz simulation | The energy and enstrophy for EMAC and SKEW agree well with each other, and with results in #REFR . | [
"For Re = 100, solutions are computed up to T = 10 on a uniform triangulation with h = 1 96 is used with a time step size of ∆t = 0.01.",
"For Re = 1000, solutions are computed up to T = 20 on a uniform triangulation with h = 1 196 and ∆t = 0.005.",
"The nonlinear problems were resolved with Newton's method, an... | [
"For momentum, the initial condition has 0 momentum in both the x and y directions; EMAC maintains this momentum up to roundoff error, while SKEW produces solutions with momentum near 10 −7 which is still quite small.",
"The plots of angular momentum versus time are quite interesting, as EMAC agrees with SKEW up ... | [
"EMAC",
"enstrophy"
] | result | {
"title": "Longer time accuracy for incompressible Navier-Stokes simulations with the EMAC formulation",
"abstract": "In this paper, we consider the recently introduced EMAC formulation for the incompressible Navier-Stokes (NS) equations, which is the only known NS formulation that conserves energy, momentum and a... | {
"title": "On reference solutions and the sensitivity of the 2D Kelvin-Helmholtz instability problem",
"abstract": "Two-dimensional Kelvin-Helmholtz instability problems are popular examples for assessing discretizations for incompressible flows at high Reynolds number. Unfortunately, the results in the literature... |
2002.01416 | 1803.06893 | 2D Kelvin-Helmholtz simulation | The plots of energy and enstrophy are in agreement with those in #REFR (after adjusting time units). | [
"The plots of angular momentum versus time are quite interesting, as EMAC agrees with SKEW up to around t = 2, at which point it deviates significantly.",
"This deviation coincides with the differences in the absolute vorticity contours in figure 7 (we show the domain extended once periodically to the right, to a... | [
"Contours of absolute vorticity for EMAC and SKEW are shown in figure 9 , and they both display qualitative behavior consistent with results of #OTHEREFR , although with some minor differences being that the max absolute vorticity for SKEW is slightly higher (notice the colorbar scale), and perhaps more important i... | [
"time units",
"enstrophy"
] | result | {
"title": "Longer time accuracy for incompressible Navier-Stokes simulations with the EMAC formulation",
"abstract": "In this paper, we consider the recently introduced EMAC formulation for the incompressible Navier-Stokes (NS) equations, which is the only known NS formulation that conserves energy, momentum and a... | {
"title": "On reference solutions and the sensitivity of the 2D Kelvin-Helmholtz instability problem",
"abstract": "Two-dimensional Kelvin-Helmholtz instability problems are popular examples for assessing discretizations for incompressible flows at high Reynolds number. Unfortunately, the results in the literature... |
2003.06972 | 1803.06893 | Consistency error bounds. | This compares well to results computed with a higher order method in #REFR for the planar case with Re = 10 4 . | [
"resulting in C K (Γ) 2 ≤ 1 2 1 .",
"Substituting this in the above estimate for the kinetic energy, we arrive at the bound E(t) ≤ E(0) exp (−8ν t) = E(0) exp −4 · 10 −5 t .",
"In Figure 7 .3 we show the kinetic energy plots for the computed solutions together with exponential fitting.",
"There are two obviou... | [] | [
"higher order method"
] | result | {
"title": "Error analysis of higher order trace finite element methods for the surface Stokes equations",
"abstract": "The paper studies a higher order unfitted finite element method for the Stokes system posed on a surface in R 3 . The method employs parametric P k -P k−1 finite element pairs on tetrahedral bulk ... | {
"title": "On reference solutions and the sensitivity of the 2D Kelvin-Helmholtz instability problem",
"abstract": "Two-dimensional Kelvin-Helmholtz instability problems are popular examples for assessing discretizations for incompressible flows at high Reynolds number. Unfortunately, the results in the literature... |
1808.04669 | 1803.06893 | Numerical results | A good agreement of the kinetic energy can be clearly seen, while the enstrophy agrees pretty well till timet = 150, where the last vortex merging toke place for our simulation, while that happens at a much later timet = 250 for the scheme used in #REFR . | [
"The numerical dissipation in our simulation triggered the last vortex merging in a much earlier time, since we use a lower order method on a coarser mesh compared with #OTHEREFR .",
"We notice that a numerical simulation at the scale of #OTHEREFR is out of reach for our desktop-based simulation.",
"However, no... | [
"Example 4: flow around a cylinder.",
"We consider the 2D-2 benchmark problem proposed in #OTHEREFR where a laminar flow around a cylinder is considered.",
"The domain is a rectangular channel without an almost vertically centered circular obstacle, c.f.",
"The boundary is decomposed into Γ in := {x = 0}, the... | [
"simulation",
"kinetic energy"
] | result | {
"title": "An explicit divergence-free DG method for incompressible flow",
"abstract": "Abstract. We present an explicit divergence-free DG method for incompressible flow based on velocity formulation only. A globally divergence-free finite element space is used for the velocity field, and the pressure field is el... | {
"title": "On reference solutions and the sensitivity of the 2D Kelvin-Helmholtz instability problem",
"abstract": "Two-dimensional Kelvin-Helmholtz instability problems are popular examples for assessing discretizations for incompressible flows at high Reynolds number. Unfortunately, the results in the literature... |
1805.01706 | 1803.06893 | Numerical tests | In addition, a qualitative comparison against benchmark data from #REFR is presented in terms of the temporal evolution of the enstrophy E(t) (here we rescale ω h with √ ν to match again the real vorticity). | [
"The characteristic time ist = δ 0 /u ∞ , the Reynolds number is Re= 10000, and the kinematic viscosity is ν = δ 0 u ∞ /Re.",
"We use a structured mesh of 128 segments per side, representing 131072 triangular elements, and we solve the problem using our first-order DG scheme, setting again the stabilisation const... | [
"We also record the evolution of the palinstrophy P (t), a quantity that encodes the dissipation process.",
"These quantities are defined, and we remark that for the palinstrophy we use the discrete gradient associated with the DG discretisation.",
"We show these quantities in Figure 5 , where also include resu... | [
"real vorticity"
] | method | {
"title": "Analysis and approximation of a vorticity-velocity-pressure formulation for the Oseen equations",
"abstract": "We introduce a family of mixed methods and discontinuous Galerkin discretisations designed to numerically solve the Oseen equations written in terms of velocity, vorticity, and Bernoulli pressu... | {
"title": "On reference solutions and the sensitivity of the 2D Kelvin-Helmholtz instability problem",
"abstract": "Two-dimensional Kelvin-Helmholtz instability problems are popular examples for assessing discretizations for incompressible flows at high Reynolds number. Unfortunately, the results in the literature... |
1909.06229 | 1803.06893 | Piecewise smooth manifolds | We can hence compare our numerical solution on Γ 0 to the results in the literature #REFR . | [
"In this subsection we consider 4 similar but different cylindrical setups in the following: is an open cylinder of height 1 with radius = (2 ) −1 , i.e.",
"perimeter 1 and we can isometrically map the unit square (periodic in -direction) on Γ 0 . On the boundary we prescribe free slip boundary condition.",
"As... | [
"Γ 1 is a corresponding closed cylinder with bottom and top added, i.e. without boundary.",
"Γ 2 is similar to Γ 1 except for the decreased height of 1 − 2 .",
"Hence, the geodesics from the center of the top of the cylinder to the center of the bottom of the cylinder have length 1.",
"The last case, case 3 c... | [
"numerical solution"
] | result | {
"title": "Divergence-free tangential finite element methods for incompressible flows on surfaces",
"abstract": "In this work we consider the numerical solution of incompressible flows on twodimensional manifolds. Whereas the compatibility demands of the velocity and the pressure spaces are known from the flat cas... | {
"title": "On reference solutions and the sensitivity of the 2D Kelvin-Helmholtz instability problem",
"abstract": "Two-dimensional Kelvin-Helmholtz instability problems are popular examples for assessing discretizations for incompressible flows at high Reynolds number. Unfortunately, the results in the literature... |
1703.05135 | 0909.2735 | Notations and Description of the Phase Transition Model | In this section we fix notations and we recall some properties concerning the 2-Phase traffic model introduced in #REFR . | [] | [
"As already said, the model (1) is an extension of the classical LWR model, given by the following scalar conservation law",
"where ρ is the traffic density and V = V (t, x, ρ) is the speed.",
"We consider the following two assumptions on the speed:",
"• We assume that, at a given density, different drivers m... | [
"2-Phase traffic model"
] | background | {
"title": "The Godunov method for a 2-phase model",
"abstract": "We consider the Godunov numerical method to the phase-transition traffic model, proposed in [1], by Colombo, Marcellini, and Rascle. Numerical tests are shown to prove the validity of the method. Moreover we highlight the differences between such mod... | {
"title": "A 2-phase traffic model based on a speed bound",
"abstract": "We extend the classical LWR traffic model allowing different maximal speeds to different vehicles. Then, we add a uniform bound on the traffic speed. The result, presented in this paper, is a new macroscopic model displaying 2 phases, based o... |
1811.02514 | 1711.04819 | III. PROPOSED UNCERTAINTY QUANTIFICATION METHODS | Firstly, we now concern the UQ strategies in general image/signal processing problems instead of just a special application in RI imaging in #REFR . | [
"Then a local credible interval (ξ −,Ωi , ξ +,Ωi ) for region Ω i is defined by #OTHEREFR where",
"N is the index operator on Ω i with value 1 for pixels in Ω i otherwise 0.",
"Note that ξ −,Ωi and ξ +,Ωi are actually the values that saturate the HPD credible region C α from above and from below at Ω i .",
"T... | [
"Secondly, here we adjust µ automatically, but #OTHEREFR assumes µ is known beforehand.",
"Finally, we consider the over-complete bases Ψ (such as SARA #OTHEREFR , #OTHEREFR ) and explore their influence in UQ with synthesis and analysis priors, which is not considered in #OTHEREFR . 1 − α 1 − α Fig. 3 . HPD cred... | [
"general image/signal processing",
"RI imaging"
] | background | {
"title": "Quantifying Uncertainty in High Dimensional Inverse Problems by Convex Optimisation",
"abstract": "Abstract-Inverse problems play a key role in modern image/signal processing methods. However, since they are generally ill-conditioned or ill-posed due to lack of observations, their solutions may have sig... | {
"title": "Uncertainty quantification for radio interferometric imaging: II. MAP estimation",
"abstract": "Uncertainty quantification is a critical missing component in radio interferometric imaging that will only become increasingly important as the big-data era of radio interferometry emerges. Statistical sampli... |
1105.4449 | 1011.1350 | 1.4. | In #REFR a geometric complexity theory (GCT) study of M M ult and its GL(V 1 ) × GL(V 2 ) × GL(V 3 ) orbit closure is considered. | [
"Connections to the GCT program.",
"The triangle case is especially interesting because we remark below that in the critical dimension case it corresponds to",
"where, setting",
",e 2 ,e 1 ∈ V 1 ⊗V 2 ⊗V 3 is the matrix multiplication operator, that is, as a tensor, M M ult e 3 ,e 2 ,e 1 = Id E 3 ⊗Id E 2 ⊗Id E... | [
"One sets e 1 = e 2 = e 3 = n and studies the geometry as n → ∞.",
"It is a toy case of the varieties introduced by Mulmuley and Sohoni #OTHEREFR 13, #OTHEREFR , letting S d C k denote the homogeneous polynomials of degree d on (C k ) * , the varieties are GL n 2 · det n ⊂ S n C n 2 and GL n 2 · ℓ n−m perm m ⊂ S ... | [
"geometric complexity theory"
] | background | {
"title": "On the geometry of tensor network states",
"abstract": "Abstract. We answer a question of L. Grasedyck that arose in quantum information theory, showing that the limit of tensors in a space of tensor network states need not be a tensor network state. We also give geometric descriptions of spaces of tens... | {
"title": "Geometric complexity theory and tensor rank",
"abstract": "Mulmuley and Sohoni [25, 26] proposed to view the permanent versus determinant problem as a specific orbit closure problem and to attack it by methods from geometric invariant and representation theory. We adopt these ideas towards the goal of s... |
1210.8368 | 1011.1350 | HWV Obstructions | But the converse is not true in general, see for instance the discussion on Strassen's invariant in #REFR . | [
",λ,i gh) = 0, which proves the proposition.",
"We call such f λ a HWV obstruction against h ∈ Gc.",
"We will show that some HWVs have a succinct encoding, which is linear in their degree d. . These properties can be rephrased as follows:",
"• There exists some HWV f λ in C[V ] of weight λ that does not vanis... | [
"Clearly, if the irreducible represenation corresponding to λ occurs in C[V ] with high multiplicity, then item one above is much harder to satisfy for occurence obstructions.",
"While Proposition 3.3 tells us that h ∈ Gc can, in principle, always be proven by exhibiting a HWV obstruction, it is unclear whether t... | [
"discussion"
] | background | {
"title": "Explicit lower bounds via geometric complexity theory",
"abstract": "We prove the lower bound R(Mm) ≥ 3 2 m 2 − 2 on the border rank of m × m matrix multiplication by exhibiting explicit representation theoretic (occurence) obstructions in the sense the geometric complexity theory (GCT) program. While t... | {
"title": "Geometric complexity theory and tensor rank",
"abstract": "Mulmuley and Sohoni [25, 26] proposed to view the permanent versus determinant problem as a specific orbit closure problem and to attack it by methods from geometric invariant and representation theory. We adopt these ideas towards the goal of s... |
1911.03990 | 1011.1350 | Result details | The proof technique is based on the technique in #REFR . The proof is postponed to Section 10. | [
"The following Proposition 4.1 writes the multiplicity mult λ * C[Gp] as a nonnegative sum of products of multi-Littlewood-Richardson coefficients and plethysm coefficients.",
"Then"
] | [
"We remark that if Problem 9 in [Sta00] is resolved positively, then Proposition 4.1 implies that the multiplicity mult λ * C[Gp] has a combinatorial description, i.e., the map (λ, m, d, D) → mult λ * C[Gp] is in #P.",
"The same holds also for its summands b(λ, ̺, D, d).",
"It is known that mult λ * C[Gq] = a λ... | [
"proof technique",
"proof"
] | method | {
"title": "Implementing geometric complexity theory: On the separation of orbit closures via symmetries",
"abstract": "Understanding the difference between group orbits and their closures is a key difficulty in geometric complexity theory (GCT): While the GCT program is set up to separate certain orbit closures, m... | {
"title": "Geometric complexity theory and tensor rank",
"abstract": "Mulmuley and Sohoni [25, 26] proposed to view the permanent versus determinant problem as a specific orbit closure problem and to attack it by methods from geometric invariant and representation theory. We adopt these ideas towards the goal of s... |
1702.07486 | 1508.00271 | Related work | An encoding scheme is also applied by #REFR , who use an encoder-recurrent-decoder (ERD) model to predict human motion amongst others. | [
"The experiments are restricted to walking, jogging and running motions.",
"Instead, we seek a more general model that can capture a large variety of actions.",
"In #OTHEREFR , a low-dimensional manifold of human motion is learned using a one-layer convolutional autoencoder.",
"For motion synthesis, the learn... | [
"The encoder-decoder framework learns to reconstruct joint angles, while the recurrent middle layer represents the temporal dynamics.",
"As the whole framework is jointly trained, the learned representation is tuned towards the dynamics of the recurrent network and might not be generalizable to new tasks.",
"Fi... | [
"human motion",
"encoder-recurrent-decoder (ERD) model"
] | method | {
"title": "Deep Representation Learning for Human Motion Prediction and Classification",
"abstract": "Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework... | {
"title": "Recurrent Network Models for Human Dynamics",
"abstract": "We propose the Encoder-Recurrent-Decoder (ERD)"
} |
1702.07486 | 1508.00271 | Motion prediction of specific actions | Note that predictions over 560 ms can diverge from the ground truth substantially due to stochasticity in human motion #REFR while remaining meaningful to a human observer. | [
"This indicates that a structural prior is beneficial to motion prediction.",
"As expected, the fine-tuning to specific actions decreases the prediction error and is especially effective during long-term prediction and for actions that are not contained in the original training data, such as \"smoking\".",
"We ... | [] | [
"human motion"
] | background | {
"title": "Deep Representation Learning for Human Motion Prediction and Classification",
"abstract": "Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework... | {
"title": "Recurrent Network Models for Human Dynamics",
"abstract": "We propose the Encoder-Recurrent-Decoder (ERD)"
} |
1908.07214 | 1508.00271 | Spatio-temporal Recurrent Neural Network (STRNN) | Note that unlike some RNNs #REFR , the decoding and predicting only start after the TEncoder takes all the input, making it a sequence-to-sequence model. | [
"It also enables us to impose constraints in a longer time span to stabilize the network.",
"The temporal network is named Two-way Bidirectional Temporal Network (TBTN), consisting of three parts: the temporal encoder (TEncoder), the temporal decoder (TDecoder) and the temporal predictor (TPredictor) (Figure 3 ).... | [
"After the encoding phase, the internal state of TEncoder is copied to TDecoder and TPredictor as a good/reasonable initialization.",
"Then, the forward pass continues on TDecoder and TPredictor simultaneously.",
"The decoding in TBTN unrolls in both directions in time.",
"The task of TDecoder is to decode th... | [
"RNNs"
] | background | {
"title": "Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling",
"abstract": "Abstract-Data-driven modeling of human motions is ubiquitous in computer graphics and computer vision applications, such as synthesizing realistic motions or recognizing actions. Recent research has shown that s... | {
"title": "Recurrent Network Models for Human Dynamics",
"abstract": "We propose the Encoder-Recurrent-Decoder (ERD)"
} |
1904.00442 | 1508.00271 | B. Human motion forecasting | In order to generate predictions for a joint (node) y starting from a given prefix sequence X pref , we build the distribution ppX|X pref , yq (see details in Section C) and we sample sequences from that posterior. Our evaluation method and metric again followed #REFR . | [
"For SpaMHMM, we used these same values of M and S and we did 3-fold cross validation on the training data of the action \"walking\" to finetune the value of λ in the range r10´4, 1s. We ended up using λ \" 0.05.",
"The number of hidden states in 1-HMM was set to 51 and in K-HMM it was set to 11 hidden states per... | [
"We fed our model with 8 prefix subsequences with 50 frames each (corresponding to 2 seconds) for each joint from the test subject and we predicted the following 10 frames (corresponding to 400 miliseconds).",
"Each prediction was built by sampling 100 sequences from the posterior and averaging.",
"We then comp... | [
"given prefix sequence",
"sequences"
] | method | {
"title": "SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities",
"abstract": "Abstract-We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology. The method is based on a mixture of shared hidden Markov mode... | {
"title": "Recurrent Network Models for Human Dynamics",
"abstract": "We propose the Encoder-Recurrent-Decoder (ERD)"
} |
1705.02082 | 1508.00271 | Related Work | Work of #REFR uses recurrent networks to predict a set of body joint heatmaps at a future frame. | [
"However, it is hard to train many mixtures of high dimensional output spaces, and, as it has been observed, many components often remain un-trained, with one component dominating the rest #OTHEREFR , unless careful mixture balancing is designed #OTHEREFR .",
"Many recent data driven approaches predict motion dir... | [
"Such representation though cannot possibly group the heatmap peaks into coherent 2D pose proposals.",
"Work of #OTHEREFR casts frame prediction as sequential conditional prediction, and samples from a categorical distribution of 255 pixel values at every pixel location, conditioning at the past history and image... | [
"recurrent networks"
] | background | {
"title": "Motion Prediction Under Multimodality with Conditional Stochastic Networks",
"abstract": "Given a visual history, multiple future outcomes for a video scene are equally probable, in other words, the distribution of future outcomes has multiple modes. Multimodality is notoriously hard to handle by standa... | {
"title": "Recurrent Network Models for Human Dynamics",
"abstract": "We propose the Encoder-Recurrent-Decoder (ERD)"
} |
1511.05298 | 1508.00271 | Human motion modeling and forecasting | We show that our structured approach outperforms the state-of-the-art unstructured deep architecture #REFR on motion forecasting from motion capture (mocap) data. | [
"Human body is a good example of separate but well related components.",
"Its motion involves complex spatiotemporal interactions between the components (arms, legs, spine), resulting in sensible motion styles like walking, eating etc.",
"In this experiment, we represent the complex motion of humans over st-gra... | [
"Several approaches based on Gaussian processes #OTHEREFR , Restricted Boltzmann Machines (RBMs) #OTHEREFR , and RNNs #OTHEREFR have been proposed to model human motion. Recently, Fragkiadaki et al.",
"#OTHEREFR proposed an encoder-RNN-decoder (ERD) which gets state-of-the-art forecasting results on H3.6m mocap d... | [
"motion forecasting"
] | method | {
"title": "Structural-RNN: Deep Learning on Spatio-Temporal Graphs",
"abstract": "Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while many problems in computer vision inherently have an underlying high-l... | {
"title": "Recurrent Network Models for Human Dynamics",
"abstract": "We propose the Encoder-Recurrent-Decoder (ERD)"
} |
1511.05298 | 1508.00271 | Human motion modeling and forecasting | The motion generated by ERD #REFR stays human-like in the short-term but it drifts away to non-human like motion in the long-term. | [
"Figure 6 shows forecasting 1000ms of human motion on \"eating\" activity -the subject drinks while walking.",
"S-RNN stays close to the ground-truth in the short-term and generates human like motion in the long-term.",
"On removing edgeRNNs, the parts of human body become independent and stops interacting thro... | [
"This was a common outcome of ERD on complex aperiodic activities, unlike S-RNN.",
"Furthermore, ERD produced human motion was non-smooth on many test examples.",
"See the video on the project web page for more examples #OTHEREFR . Quantitative evaluation. We follow the evaluation metric of Fragkiadaki et al.",... | [
"motion",
"ERD"
] | background | {
"title": "Structural-RNN: Deep Learning on Spatio-Temporal Graphs",
"abstract": "Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while many problems in computer vision inherently have an underlying high-l... | {
"title": "Recurrent Network Models for Human Dynamics",
"abstract": "We propose the Encoder-Recurrent-Decoder (ERD)"
} |
1806.08666 | 1508.00271 | BACKGROUND | For example, Fragkiadaki and colleagues #REFR proposed two architectures: LSTM-3LR (3 layers of Long ShortTerm Memory cells) and ERD (Encoder-Recurrent-Decoder) to concatenate LSTM units to model the dynamics of human motions. | [
"Therefore, we will focus our discussion on generative motion models and their application in human motion generation and control.",
"Our work builds upon a significant body of previous work on constructing generative statistical models for human motion analysis and synthesis.",
"Generative statistical motion m... | [
"Jain and colleagues #OTHEREFR introduced structural RNNs (SRNNs) for human motion prediction and generation by combining high-level spatio-temporal graphs with sequence modeling success of RNNs.",
"RNNs is appealing to human motion modeling because it can handle nonlinear dynamics and long-term temporal dependen... | [
"Encoder-Recurrent-Decoder"
] | background | {
"title": "Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control",
"abstract": "This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for... | {
"title": "Recurrent Network Models for Human Dynamics",
"abstract": "We propose the Encoder-Recurrent-Decoder (ERD)"
} |
1807.02350 | 1508.00271 | I. INTRODUCTION | These models slightly outperform the results in #REFR , have lower computational complexity once trained, and are therefore applicable to online tasks, but may overfit training data due to their deterministic mapping between subsequences. | [
"proposed a generative model for human motion generation using a deep neural architecture with Variational Inference (VI) #OTHEREFR and Bayesian filtering with Dynamic Movement Primitives (DMP) #OTHEREFR which ensures local space-time continuity in movement representation in a reduced space.",
"This latent space ... | [
"[17] proposed a method for motion prediction that outperforms #OTHEREFR by far, and is similar to #OTHEREFR , with the exception that a noise was applied to training samples, by feeding the network with its own generated predicted sequences.",
"This noise injection at training time prevents the system overfittin... | [
"training data"
] | result | {
"title": "A Variational Time Series Feature Extractor for Action Prediction",
"abstract": "Abstract-We propose a Variational Time Series Feature Extractor (VTSFE), inspired by the VAE-DMP model of Chen et al. [1] , to be used for action recognition and prediction. Our method is based on variational autoencoders. ... | {
"title": "Recurrent Network Models for Human Dynamics",
"abstract": "We propose the Encoder-Recurrent-Decoder (ERD)"
} |
1702.08212 | 1508.00271 | B. Online human motion prediction | Note that due to the stochasticity in human motion, an accurate longterm prediction (> 560 ms) is often not possible #REFR . | [
"Additionally, we report a variance estimate for each time step in the predicted time window ∆t as the average sum of variances of the limb and spatial dimensions. In Fig.",
"4 a)-c) we visualize the motion prediction errors of the torso, right arm and left arm model for the duration of 1660 ms.",
"Since the sk... | [
"For HRI it is important to represent these uncertainties about motion predictions such that the robot can take these into account during motion planning.",
"In comparison to our CVAE models, a simple linear extrapolation in Fig.",
"4 d) showcases the The samples were generated by propagating the past motion wi... | [
"human motion"
] | background | {
"title": "Anticipating many futures: Online human motion prediction and synthesis for human-robot collaboration",
"abstract": "Abstract-Fluent and safe interactions of humans and robots require both partners to anticipate the others' actions. A common approach to human intention inference is to model specific tra... | {
"title": "Recurrent Network Models for Human Dynamics",
"abstract": "We propose the Encoder-Recurrent-Decoder (ERD)"
} |
1912.10150 | 1508.00271 | Related Works | For deep-learning-based methods, RNNs are probably one of the most successful models #REFR . | [
"Restricted Boltzmann Machine (RBM) also has been applied for motion generation #OTHEREFR ).",
"However, inference for RBM is known to be particularly challenging.",
"Gaussian-process latent variable models #OTHEREFR Urtasun et al.",
"2008 ) and its variants #OTHEREFR have been applied for this task.",
"One... | [
"However, most existing models assume output distributions as Gaussian or Gaussian mixture.",
"Different from our implicit representation, these methods are not expressive enough to capture the diversity of human actions.",
"In contrast to action prediction, limited work has been done for diverse action generat... | [
"RNNs"
] | method | {
"title": "Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions",
"abstract": "Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN... | {
"title": "Recurrent Network Models for Human Dynamics",
"abstract": "We propose the Encoder-Recurrent-Decoder (ERD)"
} |
1804.10692 | 1512.03012 | Policy learning with perceptual rewards | Using the subject and object categories extracted from the natural language utterance, we retrieve corresponding 3D models from external 3D databases (3D Shapenet #REFR and 3D Warehouse [2]) and import them in a physics simulator (Bullet). | [
"Model-free policy search with binary rewards has notoriously high sample complexity due to the lack of informative gradients for the overwhelming majority of the sampled actions #OTHEREFR .",
"Efficient policy search requires shaped rewards, either explicitly #OTHEREFR , or more recently, implicitly [5] , by enc... | [
"We sample 3D locations for the objects, render the scene and evaluate the score of our detector.",
"Note that since we know the object identities, the relation module is the only one that needs to be considered for this scoring.",
"We pick the highest scoring 3D configuration as our goal configuration.",
"It... | [
"natural language utterance",
"3D Shapenet"
] | method | {
"title": "Reward Learning from Narrated Demonstrations",
"abstract": "Humans effortlessly\"program\"one another by communicating goals and desires in natural language. In contrast, humans program robotic behaviours by indicating desired object locations and poses to be achieved, by providing RGB images of goal co... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
2002.03892 | 1512.03012 | A. Dataset and Evaluation Metrics | In these experiments, we mainly used a subset of ShapeNetCore #REFR containing 500 models from five categories including Mug, Chair, Knife, Guitar, and Lamp. | [] | [
"For each category, we randomly selected 100 object models and convert them into complete point clouds with the pyntcloud package.",
"We then shift and resize the point clouds data and convert them into a 32 × 32 × 32 array as the input size of networks.",
"To the best of our knowledge, there are no existing si... | [
"Chair",
"500 models"
] | method | {
"title": "Learning to Grasp 3D Objects using Deep Residual U-Nets",
"abstract": "Affordance detection is one of the challenging tasks in robotics because it must predict the grasp configuration for the object of interest in real-time to enable the robot to interact with the environment. In this paper, we present ... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1505.05641 | 1512.03012 | 3D Model Dataset | We download 3D models from ShapeNet #REFR , which has organized common daily objects with categorization labels and joint alignment. | [
"As we discussed in Sec 2, there are several largescale 3D model repositories online."
] | [
"Since we evaluate our method on the PASCAL 3D+ benchmark, we download 3D models belonging to the 12 categories of PASCAL 3D+, including 30K models in total.",
"After symmetry-preserving model set augmentation (Sec 4.1), we make sure that every category has 10K models. For more details, please refer to supplement... | [
"3D models",
"ShapeNet"
] | method | {
"title": "Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views",
"abstract": "Object viewpoint estimation from 2D images is an essential task in computer vision. However, two issues hinder its progress: scarcity of training data with viewpoint annotations, and a lack of p... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1811.11187 | 1512.03012 | Alignment | Figure 6 : Unconstrained scenario where instead of having a ground truth set of CAD models given, we use a set of 400 randomly selected CAD models from ShapeNetCore #REFR , more closely mimicking a real-world application scenario. | [
"6 shows the capability of our method to align in an unconstrained real-world setting where ground truth CAD models are not given, we instead provide a set of 400 random CAD models from ShapeNet #OTHEREFR . #OTHEREFR scenes.",
"Our approach to learning geometric features between real and synthetic data produce mu... | [] | [
"CAD models"
] | method | {
"title": "Scan2CAD: Learning CAD Model Alignment in RGB-D Scans",
"abstract": "Figure 1: Scan2CAD takes as input an RGB-D scan and a set of 3D CAD models (left). We then propose a novel 3D CNN approach to predict heatmap correspondences between the scan and the CAD models (middle). From these predictions, we form... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1907.09381 | 1512.03012 | Implementation details | From ShapeNet #REFR , we select 401 different classes of vehicles and, for each vehicle, we screenshot each rendered image from 80 different viewpoints. | [
"3D model pool."
] | [
"Since the background of the rendered image is very clean, we can simply extract the accurate silhouettes by thresholding.",
"In this way, we collect 32,080 silhouettes to form the auxiliary 3D model pool.",
"Network structure and training.",
"In practice, as encoder-decoder structure, both G 1 and G 2 downsa... | [
"vehicle",
"ShapeNet"
] | method | {
"title": "Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery",
"abstract": "In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, firstly, to improve the quality... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1803.08457 | 1512.03012 | 3-D Point Cloud Clustering | For these experiments, we use objects from ShapeNet, #REFR which are sampled to create point clouds with 2048 points. | [
"Contrary to the datasets we have shown so far, the feature representation of the point clouds must be permutation-invariant and the reconstruction should match the shape outline and not the exact point coordinates.",
"Therefore, a different autoencoder architecture and loss need to be used.",
"We use the archi... | [
"The autoencoder is first trained for 1000 iterations using an Adam optimizer with a learning rate of 0.0005.",
"During the clustering stage, the autoencoder learning rate is set to 0.0001 and the learning rate of U is set to 0.0001.",
"The number of epochs between m update is set to 30."
] | [
"ShapeNet"
] | method | {
"title": "Clustering-Driven Deep Embedding With Pairwise Constraints",
"abstract": "Recently, there has been increasing interest to leverage the competence of neural networks to analyze data. In particular, new clustering methods that employ deep embeddings have been presented. In this paper, we depart from centr... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1812.02725 | 1512.03012 | Introduction | This advantage allows us to leverage both 2D image datasets and 3D shape collections #REFR and to synthesize objects of diverse shapes and texture. | [
"Finally, it learns to add diverse, realistic texture to 2.5D sketches and produce 2D images that are indistinguishable from real photos. We call our model Visual Object Networks (VON).",
"32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada. #OTHEREFR .",
"(b) Our model pro... | [
"Through extensive experiments, we show that VON produce more realistic image samples than recent 2D deep generative models.",
"We also demonstrate many 3D applications that are enabled by our disentangled representation, including rotating an object, adjusting object shape and texture, interpolating between two ... | [
"2D image datasets",
"3D shape collections"
] | background | {
"title": "Visual Object Networks: Image Generation with Disentangled 3D Representation",
"abstract": "Recent progress in deep generative models has led to tremendous breakthroughs in image generation. However, while existing models can synthesize photorealistic images, they lack an understanding of our underlying... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1907.13236 | 1512.03012 | Introduction | Since collecting a large dataset with ground truth annotations is expensive and time-consuming, it is appealing to utilize synthetic data for training, such as using the ShapeNet repository which contains thousands of 3D shapes of different objects #REFR . | [
"A common environment in which manipulation tasks take place is on tabletops.",
"Thus, in this paper, we approach this by focusing on the problem of unseen object instance segmentation (UOIS), where the goal is to separately segment every arbitrary (and potentially unseen) object instance, in tabletop environment... | [
"However, there exists a domain gap between synthetic data and real world data.",
"Training directly on synthetic data only usually does not work well in the real world #OTHEREFR .",
"Consequently, recent efforts in robot perception have been devoted to the problem of Sim2Real, where the goal is to transfer cap... | [
"ground truth annotations",
"ShapeNet repository"
] | method | {
"title": "The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation",
"abstract": "Abstract: In order to function in unstructured environments, robots need the ability to recognize unseen novel objects. We take a step in this direction by tackling the problem of segmentin... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1808.09351 | 1512.03012 | Implementation Details | For object meshes, we choose eight CAD models from ShapeNet #REFR including cars, vans, and buses. | [
"Semantic branch.",
"Our semantic branch adopts Dilated Residual Networks (DRN) for semantic segmentation . We train the network for 25 epochs.",
"Geometric branch.",
"We use Mask-RCNN for object proposal generation #OTHEREFR ."
] | [
"Given an object proposal, we predict its scale, rotation, translation, 4 3 FFD grid point coefficients, and an 8-dimensional distribution across candidate meshes with a ResNet-18 network .",
"The translation t can be recovered using the estimated offset e, the normalized distance log τ , and the ground truth foc... | [
"ShapeNet"
] | method | {
"title": "3D-Aware Scene Manipulation via Inverse Graphics",
"abstract": "We aim to obtain an interpretable, expressive, and disentangled scene representation that contains comprehensive structural and textural information for each object. Previous scene representations learned by neural networks are often uninte... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1803.07289 | 1512.03012 | Experiments | To evaluate the effectiveness of our approach, we participate in two benchmarks that arise from the ShapeNet #REFR dataset, which consists of synthetic 3D models created by digital artists. | [
"We conducted several experiments to validate our approach.",
"These show that our flex-convolution-based neural network yields competitive performance to previous work on synthetic data for single object classification ( #OTHEREFR , 1024 points) using fewer resources and provide some insights about human perform... | [] | [
"ShapeNet dataset"
] | method | {
"title": "Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)",
"abstract": "Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid. However, unstructured data like 3D point clouds containing irregular neighbor... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1806.04807 | 1512.03012 | Network Architecture Details | Our results are poorer on the 'Scene11' dataset, because the images there are synthesized with random objects from the ShapeNet #REFR without physically correct scale. | [
"APPENDIX C: EVALUATION ON DEMON DATASET Table 5 summarizes our results on the DeMoN dataset.",
"For a comparison, we also cite the results from DeMoN #OTHEREFR and the most recent work LS-Net .",
"We further cite the results from some conventional approaches as reported in DeMoN, indicated as Oracle, SIFT, FF,... | [
"This setting is inconsistent with real data and makes it harder for our method to learn the basis depth map generator.",
"When compared with LS-Net , our method achieves similar accuracy on camera poses but better scene depth.",
"It proves our feature-metric BA with learned feature is superior than the photome... | [
"'Scene11' dataset",
"ShapeNet"
] | method | {
"title": "BA-Net: Dense Bundle Adjustment Network",
"abstract": "This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error. The whole pipelin... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1912.05237 | 1512.03012 | Experiments | Dataset: We render synthetic datasets using objects from ShapeNet #REFR , considering three datasets with varying difficulty. | [
"In this section, we first compare our approach to several baselines on the task of 3D controllable image generation, both on synthetic and real data.",
"Next, we conduct a thorough ablation study to better understand the influence of different representations and architecture components."
] | [
"Two datasets contain cars, one with and the other without background.",
"For both datasets, we randomly sample 1 to 3 cars from a total of 10 different car models.",
"Our third dataset is the most challenging of these three.",
"It comprises indoor scenes containing objects of different categories, including ... | [
"ShapeNet"
] | method | {
"title": "Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis",
"abstract": "In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be rep... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1809.05068 | 1512.03012 | Training Paradigm | Our 2.5D sketch estimation network and 3D completion network are trained with images rendered with ShapeNet #REFR objects (see Sections 4.1 and 5 for details). | [
"We train our network in two stages.",
"We first pre-train the three components of our model separately.",
"The shape completion network is then fine-tuned with both voxel loss and naturalness losses."
] | [
"We train the 2.5D sketch estimator using a L2 loss and SGD with a learning rate of 0.001 for 120 epochs.",
"We only use the supervised loss L voxel for training the 3D estimator at this stage, again with SGD, a learning rate of 0.1, and a momentum of 0.9 for 80 epochs.",
"The naturalness network is trained in ... | [
"3D completion network",
"ShapeNet objects"
] | method | {
"title": "Learning Shape Priors for Single-View 3D Completion and Reconstruction",
"abstract": "Abstract. The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objec... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1802.09292 | 1512.03012 | C. Render Pipeline | Render Pipeline for 3D Category-level Models: First, we choose a collection of CAD models of chairs, comprising of about 250 chairs sampled from the ShapeNet #REFR repository. | [
"Inspired by RenderForCNN #OTHEREFR , we implement our customized render pipeline for generating huge amounts of synthetic keypoint annotated chair images using a small set of 3D annotated keypoints.",
"We briefly summarize the steps in our render pipeline, and how we exploit its advantages for learning 3D catego... | [
"For each chair, we synthesize a few (typically 8) 2D images with predetermined viewpoints (azimuth, elevation, and camera-tilt angles).",
"Keypoints in these images are then annotated (in 2D) manually, and then triangulated to 3D to obtain 3D keypoint locations on the CAD model.",
"Since the models are already... | [
"3D Category-level Models",
"ShapeNet repository"
] | method | {
"title": "Constructing Category-Specific Models for Monocular Object-SLAM",
"abstract": "We present a new paradigm for real-time objectoriented SLAM with a monocular camera. Contrary to previous approaches, that rely on object-level models, we construct category-level models from CAD collections which are now wid... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1703.04079 | 1512.03012 | Rigid or man-made shapes | We create data for cars and aeroplanes mesh models from the ShapeNet database #REFR to feed into our neural network architecture. | [] | [
"We discuss the preprocessing steps and the correspondence development to create robust geometry image data for these synsets.",
"Preprocessing: There are two constraints for the spherical parametrization technique of #OTHEREFR to work on a mesh model.",
"First, the surface mesh needs to follow the Euler charac... | [
"ShapeNet database"
] | method | {
"title": "SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks",
"abstract": "3D shape models are naturally parameterized using vertices and faces, i.e., composed of polygons forming a surface. However, current 3D learning paradigms for predictive and generative tasks using convolutional neural netw... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1906.01568 | 1512.03012 | Generalization to other objects | For car images we render ShapeNet's #REFR synthetic car models from various viewpoints and textures. | [
"To understand the generalization of the method to other symmetric objects, we train on two additional datasets.",
"We use the cat dataset provided by #OTHEREFR , crop the cat heads using the keypoint annotations and split the images by 8:1:1 into train, validation and test sets."
] | [
"We are able to reconstruct both object categories well and the results are visualized in fig. 4 .",
"Although we assume Lambertian surfaces to estimate the shading, our model can reconstruct cat faces convincingly despite their fur which has complicated light transport mechanics.",
"This shows that the other p... | [
"ShapeNet's synthetic car"
] | method | {
"title": "Photo-Geometric Autoencoding to Learn 3D Objects from Unlabelled Images",
"abstract": "We show that generative models can be used to capture visual geometry constraints statistically. We use this fact to infer the 3D shape of object categories from raw single-view images. Differently from prior work, we... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
2003.12753 | 1512.03012 | A Baseline Approach for Single-view Reconstruction | Unlike the general objects in ShapeNet #REFR , the garment shape typically appears as a thin layer with open boundary. | [
"To demonstrate the usefulness of Deep Fashion3D, we propose a novel baseline approach for single-view garment reconstruction.",
"Specifically, taking a single image I of a garment as input, we aim to reconstruct its 3D shape represented as a triangular mesh.",
"Although recent advances in 3D deep learning tech... | [
"While implicit representation #OTHEREFR can only model closed surface, voxel based approach #OTHEREFR is not suited for recovering shell-like structure like the garment surface. (2) Complex shape topologies.",
"As all existing mesh-based approaches #OTHEREFR rely on deforming a fixed template, they fail to handl... | [
"garment shape",
"ShapeNet"
] | background | {
"title": "Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images",
"abstract": "High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc. Recent advances in learning-based approach... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1701.06507 | 1512.03012 | Training data | Shape geometry comprises of 300 random cars from from ShapeNet #REFR . Note that the models were assumed to be upright. | [
"Training data comprises of synthetic images that show a random shape, with partially random reflectance shaded by random environment map illumination.",
"Shape."
] | [
"This class was chosen, as it presents both smooth surfaces as well as hard edges typical for mechanical objects.",
"Note that our results show many classes very different from cars, such as fruits, statues, mechanical appliances, etc.",
"Please note that we specifically restricted training to only cars to eval... | [
"ShapeNet"
] | method | {
"title": "Plausible Shading Decomposition For Layered Photo Retouching",
"abstract": "Figure 1: Our approach automatically splits input images into layers motivated by light transport, such as (a): occlusion, albedo, irradiance and specular, or (b): the six major spatial light directions, which can then be manipu... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
2004.00543 | 1512.03012 | Results and Discussion | We choose couch, chair, table, bike, and canoe as six common object classes, and we take the object meshes from ShapeNet #REFR . | [
"For a more concrete comparison, we consider a variant of PIXOR using PointPillar's pillar representation instead of voxelization and keep the backbone architecture identical.",
"We compare this variant, PIXOR* against PIXOR and show the results in Figure 8 .",
"Here, we can see that with even with identical ba... | [
"We apply uniform mesh re-sampling in meshlab #OTHEREFR to reduce the number of faces and produce regular geometry prior to deformation.",
"In these experiments we limit the maximum vertex perturbation to 0.03m so that the adversary will resemble the common object, and limit translation to 0.1m, and allow free ro... | [
"ShapeNet"
] | method | {
"title": "Physically Realizable Adversarial Examples for LiDAR Object Detection",
"abstract": "Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial attacks with visually imperceptible per... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1809.05070 | 1512.03012 | Decomposing Tools | Because of the absence of tool data in the ShapeNet Core #REFR dataset, we download the tools from 3D Warehouse and manually remove all unrelated models. | [
"We then demonstrate the practical applicability of our model by decomposing synthetic real-world tools. Table 4 . Quantitative results of physical parameter estimation on tools.",
"Combining visual appearance with physics observations helps our model to perform much better on physical parameter estimation, and c... | [
"In total, there are 204 valid tools, and we use Blender to remesh and clean up these tools to fix the issues with missing faces and normals. Following Chang et al .",
"#OTHEREFR , we perform PCA on the point clouds and align models by their PCA axes.",
"Sample tools in our dataset are shown in Figure 6 . Primi... | [
"tool data",
"ShapeNet Core dataset"
] | method | {
"title": "Physical Primitive Decomposition",
"abstract": "Abstract. Objects are made of parts, each with distinct geometry, physics, functionality, and affordances. Developing such a distributed, physical, interpretable representation of objects will facilitate intelligent agents to better explore and interact wi... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1908.06277 | 1512.03012 | Previous work | Propelled by the availability of large scale CAD collections such as ShapeNet #REFR and the increase in GPU parallel computing capabilities, learning based solutions have become the method of choice for reconstructing 3D shapes from single images. | [] | [
"Generally speaking, the 3D representations currently in use fall into three main categories: (i) grid based methods, such as voxel, which are 3D extensions of Pixels, (ii) topology preserving geometric methods, such as polygon meshes, and (iii) un-ordered geometric structures such as point clouds.",
"Grid based ... | [
"ShapeNet"
] | method | {
"title": "Deep Meta Functionals for Shape Representation",
"abstract": "We present a new method for 3D shape reconstruction from a single image, in which a deep neural network directly maps an image to a vector of network weights. The network parametrized by these weights represents a 3D shape by classifying ever... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1612.00404 | 1512.03012 | Experiments | We perform our experiments primarily using the ShapeNet #REFR dataset which has a large collection of 3D models. | [
"Dataset."
] | [
"In particular, we use the 'airplane' and 'chair' object categories which have thousands of meshes available.",
"The ShapeNet models are already aligned in a canonical frame and are of a fixed scale.",
"Additionally, in order to demonstrate applicability beyond rigid objects, we also manually download and simil... | [
"ShapeNet dataset"
] | method | {
"title": "Learning Shape Abstractions by Assembling Volumetric Primitives",
"abstract": ": Examples of chair and animal shapes assembled by composing simple volumetric primitives (cuboids). The obtained reconstructions allows an interpretable representation for each object and provides a consistent parsing across... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
2002.10342 | 1512.03012 | III. METHOD | The scenario for our experimental comparison is table-top reconstruction and semantic labelling of a scene containing scattered objects, selected from a number of ShapeNet categories #REFR , as a depth camera browses the scene in an adhoc way. | [] | [
"Since our focus is on a fundamental comparison of view-based and map-based labelling, we choose a height map representation for our scenes whose 2.5D nature allows us to use the same CNN network architecture designed for RGB-D input for both labelling methods.",
"We use Height Map Fusion #OTHEREFR as our scene r... | [
"semantic labelling",
"ShapeNet categories"
] | method | {
"title": "Comparing View-Based and Map-Based Semantic Labelling in Real-Time SLAM",
"abstract": "Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels. The many approaches to labelling scenes can be divided into two cle... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1903.10170 | 1512.03012 | Shape transform results and ablation studies | The first domain pair on which we test our network is the chair and table datasets from ShapeNet #REFR , which contain mesh models. | [] | [
"The chair dataset consists of 4,768 training shapes and 2,010 test shapes, while the table dataset has 5,933 training shapes and 2,526 test shapes.",
"We normalize each chair/table mesh to make the diagonal of its bounding box equal to unit length and sample the normalized mesh uniformly at random to obtain 2,04... | [
"ShapeNet"
] | method | {
"title": "LOGAN: Unpaired Shape Transform in Latent Overcomplete Space",
"abstract": "We present LOGAN, a deep neural network aimed at learning generic shape transforms from unpaired domains. The network is trained on two sets of shapes, e.g., tables and chairs, but there is neither a pairing between shapes in th... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
2003.12397 | 1512.03012 | Related Work | However, it only handles synthetic 3D shapes composed of the most basic geometries, while our method is evaluated on ShapeNet #REFR models. | [
"grammar parsing for shape analysis and modeling. Teboul et al.",
"#OTHEREFR use RL to parse the shape grammar of the building facade. Ruiz-Montiel et al.",
"#OTHEREFR propose an approach to complement the generative power of shape grammars with reinforcement learning techniques.",
"These methods all focus on... | [
"High-level shape understanding There has been growing interest in highlevel shape analysis, where the ideas are central to part-based segmentation #OTHEREFR and structure-based shape understanding #OTHEREFR .",
"Primitive-based shape abstraction #OTHEREFR , in particular, is well-researched for producing structu... | [
"synthetic 3D shapes",
"ShapeNet models"
] | method | {
"title": "Modeling 3D Shapes by Reinforcement Learning",
"abstract": "We explore how to enable machines to model 3D shapes like human modelers using reinforcement learning (RL). In 3D modeling software like Maya, a modeler usually creates a mesh model in two steps: (1) approximating the shape using a set of primi... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1803.07252 | 1512.03012 | VI. EXPERIMENTAL RESULTS | We first empirically tune parameters on a small dataset with 8 models, then generalize the parameter setting learned from the small dataset to a larger dataset, i.e., 100 models from the ShapeNetCore dataset #REFR for validation. | [
"The proposed scheme GLR is compared with existing works covering the four categories of point cloud denoising methods mentioned in Section II: APSS #OTHEREFR and RIMLS #OTHEREFR for MLS-based methods, AWLOP #OTHEREFR for LOP-based methods, MRPCA #OTHEREFR for sparsity-based methods, non-local denoising (NLD) algor... | [
"Comparison with existing methods on both dataset are detailed as follows."
] | [
"ShapeNetCore dataset"
] | method | {
"title": "3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model",
"abstract": "Abstract-3D point cloud-a new signal representation of volumetric objects-is a discrete collection of triples marking exterior object surface locations in 3D space. Conventional imperfect acq... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1604.06079 | 1512.03012 | Experimental Results | In this paper, we propose to generate the ground-truth data through rendering ShapeNet models #REFR . | [
"We consider four popular object categories where the underlying reflectional symmetry is salient: chair, car, table, and sofa.",
"An important challenge is to obtain ground-truth data to train each individual network.",
"Standard dataset creation approaches such as human labeling or scanning are inappropriate ... | [
"We employ an open-source physically-based rendering software, Mitsuba, to generate realistic renderings.",
"We use 700−2500 models for each category to generate training data.",
"For each selected object, we choose 36 random views, each of which provides an image with ground-truth geometric information.",
"F... | [
"ShapeNet models"
] | method | {
"title": "DeepSymmetry: Joint Symmetry and Depth Estimation using Deep Neural Networks",
"abstract": "Abstract. Due to the abundance of 2D product images from the internet, developing efficient and scalable algorithms to recover the missing depth information is central to many applications. Recent works have addr... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1904.00230 | 1512.03012 | Dataset Details | This synthetic dataset consists of about 32000 3D CAD models belonging to 16 shape categories from the original ShapeNetCore 3D data repository #REFR . | [
"vKITTI #OTHEREFR .",
"Virtual-KITTI (vKITTI) is a synthetic large outdoor dataset with 13 semantic classes from urban scenes. vKITTI imitates data from the real-world KITTI dataset.",
"It contains data from 5 different simulated worlds, resulting in 50 high resolution scenes.",
"This dataset is used for a va... | [
"Each point in a 3D model is annotated with a part label (e.g.",
"a plane is segmented into body, wing, engine, and tail parts).",
"We consider the subset used for the ShapeNet part segmentation challenge, which contains 17775 models with 50 parts in total.",
"Each model is normalized into the 3D cube [−1, 1]... | [
"16 shape categories"
] | method | {
"title": "MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds",
"abstract": "We present a self-supervised task on point clouds, in order to learn meaningful point-wise features that encode local structure around each point. Our self-supervised network, named MortonNet, operates directly on un... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1802.00411 | 1512.03012 | Overview | To generate ground truth training and evaluation pairs, we virtually scan 3D objects from ShapeNet #REFR . Fig. | [
"To achieve this task, each object model is represented by a high resolution 3D voxel grid.",
"We use the simple occupancy grid for shape encoding, where 1 represents an occupied cell and 0 an empty cell.",
"Specifically, the input 2.5D partial view, denoted as x x, is a 64 3 occupancy grid, while the output 3D... | [
"1 is the t-SNE visualization #OTHEREFR of partial 2.5D views and the corresponding full 3D shapes for multiple general chair and bed models.",
"Each green dot represents the t-SNE embedding of a 2.5D view, whilst a red dot is the embedding of the corresponding 3D shape.",
"It can be seen that multiple categori... | [
"ShapeNet"
] | method | {
"title": "Dense 3D Object Reconstruction from a Single Depth View",
"abstract": "In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically re... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1807.03407 | 1512.03012 | Latent Denoising Optimization with DAE (DAE+LDO): | We use ShapeNetCore, a subset of the full ShapeNet #REFR dataset with manually verified category and alignment annotations. | [
"To show the transferability of LDO, we also apply it on DAE, with a GAN trained on GFVs produced by the DAE on clean training data.",
"We show that LDO is able to capitalize on the more robust representations learnt by DAE to improve performance even further than AE+LDO.",
"Dataset."
] | [
"It covers 55 common object categories with about 51,300 unique 3D models.",
"For the purposes of our experiments, we use 4 classes with the most available data from the dataset, namely: airplane, car, chair and table.",
"For each class, we split the models into 85/5/10 train-validation-test sets for our experi... | [
"full ShapeNet"
] | method | {
"title": "High Fidelity Semantic Shape Completion for Point Clouds Using Latent Optimization",
"abstract": "Semantic shape completion is a challenging problem in 3D computer vision where the task is to generate a complete 3D shape using a partial 3D shape as input. We propose a learning-based approach to complete... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
2003.12641 | 1512.03012 | Architecture | The network was implemented using four 1D convolution layers of sizes [256, 256, 128, #REFR . | [
"For a feature extractor, we used DGCNN #OTHEREFR with the same configurations as in the official PyTorch implementation: Four point-cloud convolution layers of sizes [64, 64, 128, 256] respectively and a 1D convolution layer with kernel size 1 (featurewise fully connected) with a size of 1024 before extracting a g... | [
"We applied batch normalization [18] after all convolution layers and used leaky relu activation with a slope of 0.2."
] | [
"four 1D convolution",
"network"
] | method | {
"title": "Self-Supervised Learning for Domain Adaptation on Point-Clouds",
"abstract": "Self-supervised learning (SSL) allows to learn useful representations from unlabeled data and has been applied effectively for domain adaptation (DA) on images. It is still unknown if and how it can be leveraged for domain ada... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1904.00817 | 1512.03012 | Dataset | We construct our dataset from the Shapenet Core dataset #REFR with nearly 17, 000 shapes from 16 categories. | [] | [
"The dataset consists of labelled corresponding parts across various segmented 3D models.",
"Therefore, we construct a set of corresponding pairs using the part-based registration from #OTHEREFR .",
"Further, we extract ISS keypoints #OTHEREFR from the resultant parts and add to dataset, the points which have a... | [
"Shapenet Core"
] | method | {
"title": "DeepPoint3D: Learning Discriminative Local Descriptors using Deep Metric Learning on 3D Point Clouds",
"abstract": "Learning local descriptors is an important problem in computer vision. While there are many techniques for learning local patch descriptors for 2D images, recently efforts have been made f... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1907.09786 | 1512.03012 | C. Ablation studies | However, we must clarify that there exists a major difference between the datasets of two tasks: unlike the 2-D road layout hallucinating, the prior knowledge and partial observation samples are from an identical distribution (ShapeNet dataset #REFR ) in the 3-D vehicle shape completion task. | [
"The performance without the observation pair degrades in terms of the contour accuracy with certain margins (-2.4% and -2.1% F -measure), due to the fact that the observation pair provides a clear supervision at boundary regions.",
"With three supervisions, the network exhibits the best contour accuracy and an o... | [
"This leads to some different observations in this ablation studies: 1) without the masked prior knowledge pair, the performance still remains optimal, and 2) adding the masked prior knowledge pair to the pre-selection pair degrades the performance.",
"Due to the absence of the domain gap between the partially ob... | [
"ShapeNet"
] | background | {
"title": "Hallucinating Beyond Observation: Learning to Complete with Partial Observation and Unpaired Prior Knowledge",
"abstract": "We propose a novel single-step training strategy that allows convolutional encoder-decoder networks that use skip connections, to complete partially observed data by means of hallu... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1911.11130 | 1512.03012 | Setup | For cars, we render 35k images of synthetic cars from ShapeNet #REFR with random viewpoints and illumination, and randomly split them by 8:1:1 into train, validation and test sets. | [
"We follow the protocol of #OTHEREFR to generate a dataset, sampling shapes, poses, textures, and illumination randomly.",
"We use images from SUN Database #OTHEREFR as background and save ground truth depth maps for evaluation.",
"We also test our method on cat faces and synthetic cars. We use two cat datasets... | [
"Metrics.",
"Since the scale of 3D reconstruction from projective cameras is inherently ambiguous #OTHEREFR , we discount it in the evaluation.",
"Specifically, given the depth map d predicted by our model in the canonical view, we warp it to a depth mapd in the actual view using the predicted viewpoint and com... | [
"ShapeNet"
] | method | {
"title": "Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild",
"abstract": "We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth,... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1904.01326 | 1512.03012 | Experiment settings | For this dataset, because of the limited number of ShapeNet #REFR 3D chair models (6778 shapes), we render images from 60 randomly sampled views for each chair. | [
"Data We train HoloGAN using a variety of datasets: Basel Face #OTHEREFR , CelebA #OTHEREFR , Cats #OTHEREFR , Chairs #OTHEREFR , Cars #OTHEREFR , and LSUN bedroom #OTHEREFR .",
"We train HoloGAN on resolutions of 64×64 pixels for Cats and Chairs, and 128×128 pixels for Basel Face, CelebA, Cars and LSUN bedroom."... | [
"During training, we ensure that each batch contains completely different types of chairs to prevent the network from using set supervision, i.e., looking at the same chair from different viewpoints in the same batch, to cheat."
] | [
"ShapeNet 3D chair"
] | method | {
"title": "HoloGAN: Unsupervised Learning of 3D Representations From Natural Images",
"abstract": ". HoloGAN learns to separate pose from identity (shape and appearance) only from unlabelled 2D images without sacrificing the visual fidelity of the generated images. All results shown here are sampled from HoloGAN f... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
2003.03551 | 1512.03012 | With the availability of large-scale 3D shape database #REFR , shape information can be efficiently encoded in a deep neural network, enabling faithful 3D reconstruction even from a single image. | [
"by the input views.",
"Such limitation causes the single-view reconstruction particularly tricky due to the lack of correspondence with other views and large occlusions."
] | [
"Although many 3D representations (such as voxel-based and point cloud representations), have been utilized for 3D reconstruction, they are not efficient to express the surface details of the shape and may generate part-missing or broken structures due to the high computational cost and memory storage.",
"On the ... | [
"large-scale 3D shape",
"shape information"
] | background | {
"title": "STD-Net: Structure-preserving and Topology-adaptive Deformation Network for 3D Reconstruction from a Single Image",
"abstract": "3D reconstruction from a single view image is a long-standing problem in computer vision. Various methods based on different shape representations (such as point cloud or volu... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... | |
1812.06861 | 1512.03012 | Datasets | MovingObjects3D: For the purpose of systematically evaluating highly varying object motions, we downloaded six categories of 3D models from ShapeNet #REFR . | [
"We systematically train and evaluate our method on four datasets which we now briefly describe."
] | [
"For each object category, we rendered 200 video sequences with 100 frames in each sequence using Blender.",
"We use data rendered from the categories 'boat' and 'motorbike' as test set and data from categories 'aeroplane', 'bicycle', 'bus', 'car' as training set.",
"From the training set we use the first 95% o... | [
"ShapeNet"
] | method | {
"title": "Taking a Deeper Look at the Inverse Compositional Algorithm",
"abstract": "In this paper, we provide a modern synthesis of the classic inverse compositional algorithm for dense image alignment. We first discuss the assumptions made by this well-established technique, and subsequently propose to relax th... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1904.06699 | 1512.03012 | Introduction | By modeling the uncertainty in single-view reconstruction via a partially supervised architecture, our model achieves state-of-the-art 3D reconstruction test error on ShapeNetCore #REFR dataset. | [
"Furthermore, we propose a synthesis pipeline to transfer the single-view conditional model onto the task of multiview shape generation.",
"Different from most existing methods which utilize a recurrent unit to ensemble multi-view features, we consider multi-view reconstruction as taking the intersection of the p... | [
"Detailed ablation studies are performed to show the effectiveness of our proposed pipeline.",
"Additional experiments demonstrate that our generative approach has promising generalization ability on real world images."
] | [
"single-view reconstruction"
] | method | {
"title": "Conditional Single-View Shape Generation for Multi-View Stereo Reconstruction",
"abstract": "In this paper, we present a new perspective towards image-based shape generation. Most existing deep learning based shape reconstruction methods employ a single-view deterministic model which is sometimes insuff... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1904.06699 | 1512.03012 | Multi-category Experiments: | We tested our model in multi-category experiments following [4] on 13 popular categories on ShapeNet #REFR dataset. | [] | [
"As shown in Table 3 , our proposed method outperforms two baseline methods 3D-R2N2 #OTHEREFR and PSGN #OTHEREFR by a relatively large margin.",
"Qualitative Results: For qualitative analysis, in Figure 7 we visualize the predicted shapes for two state-of-the-art baseline methods: 3D-R2N2 #OTHEREFR and PSGN #OTHE... | [
"ShapeNet dataset"
] | method | {
"title": "Conditional Single-View Shape Generation for Multi-View Stereo Reconstruction",
"abstract": "In this paper, we present a new perspective towards image-based shape generation. Most existing deep learning based shape reconstruction methods employ a single-view deterministic model which is sometimes insuff... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1904.06699 | 1512.03012 | Ablation Studies | Al- though the shape in ShapeNet #REFR dataset often has symmetric structure, the conditional generative model outperforms the deterministic counterpart by 0.25 on CD. | [
"Conditional vs.",
"Deterministic: To demonstrate the effectiveness of the conditional model, we implemented a deterministic model S = f d (I).",
"For fair comparison, we used an encoder-decoder structure similar with our network and trained the deterministic model for two stages with the front constraint. Sing... | [
"Analysis on different features in the framework: We performed ablation analysis on three different features: twostage training, diversity constraint at multi-view training stage and consistency loss during inference.",
"As shown in Table 5 , all features achieve consistent gain on the final performance.",
"Fro... | [
"ShapeNet"
] | method | {
"title": "Conditional Single-View Shape Generation for Multi-View Stereo Reconstruction",
"abstract": "In this paper, we present a new perspective towards image-based shape generation. Most existing deep learning based shape reconstruction methods employ a single-view deterministic model which is sometimes insuff... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1910.14442 | 1512.03012 | B. Interactive Gibson Assets | The annotator is queried to retrieve the most similar CAD model from a list of possible shapes from ShapeNet #REFR . | [
"In areas with low reconstruction precision, the automatic instance segmentation results may contain duplicates as well as missing entries. These were manually corrected by in-house annotators ( Fig. 3.4 ).",
"In total, over 4,000 objects proposals resulted from this stage.",
"Object Alignment: The goal of this... | [
"Then, the human has to annotate at least 6 keypoint correspondences between the CAD model and the scan object ( Fig. 3.4) .",
"The scale and pose alignment is solved by minimizing the point-to-point distance among correspondences over seven parameters of a transformation matrix: scale (three), position (three), ... | [
"ShapeNet"
] | method | {
"title": "Interactive Gibson: A Benchmark for Interactive Navigation in Cluttered Environments",
"abstract": "We present Interactive Gibson, the first comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interaction with objects is allowed and even... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1705.10904 | 1512.03012 | Ablation Study on ShapeNet [5] | In this section, we perform ablation study and compare McRecon with the baseline methods on the ShapeNet #REFR dataset. | [] | [
"The synthetic dataset allows us to control external factors such as the number of viewpoints, quality of mask and is ideal for ablation study.",
"Specifically, we use the renderings from #OTHEREFR since it contains a large number of images from various viewpoints and the camera model has more degree of freedom."... | [
"ShapeNet dataset"
] | method | {
"title": "Weakly Supervised 3D Reconstruction with Adversarial Constraint",
"abstract": "Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks. However, this increase in performance requires large scale annotations of 2D/3D data. In this paper, we explore inexpe... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1608.05137 | 1512.03012 | CAD Model Alignment | Specifically, we consider all 3D models in the ShapeNet repository #REFR associated with our object categories of interest, i.e., chair, table, sofa, bookshelf, bed, night table, chest yielding 9193 models in total. | [
"The object detection results from Section 3.3 identify the presence of a \"chair\" (e.g.,) in a certain region of the image with high probability.",
"Now we wish to determine what kind of chair it is, its shape, and approximate 3D pose.",
"Inspired by #OTHEREFR , we solve this retrieval problem by searching fo... | [
"Each 3D model is rendered to 32 quantized viewpoints, consisting of 16 uniformly sampled azimuth angles and two elevation angles (15 and 30 degrees above horizontal).",
"Robust comparison of photos with CAD models renderings is not straightforward; simple norms like L2 do not work well in practice, due to differ... | [
"3D models",
"ShapeNet repository"
] | method | {
"title": "IM 2 CAD",
"abstract": ": We introduce IM2CAD, a new system that takes a single photograph of a real scene (left), and automatically reconstructs a 3D CAD model (right) that is similar to the real scene. Given a single photo of a room and a large database of furniture CAD models, our goal is to reconstr... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
2003.02920 | 1512.03012 | Segmentation | The segmentation methods based on point cloud obtained good results and maintained the same level with the results on ShapeNet #REFR . | [
"Methods based on points."
] | [
"SO-Net showed excellent performance on IOU and DSC of aneurysms, while PointConv had the best result on parent blood vessels.",
"PN++ had the third-best performance and had the fastest training speed (5s per epoch, and converged at approximately an epoch of 115 on GTX 1080 Ti).",
"Meanwhile, PointCNN had the s... | [
"ShapeNet"
] | result | {
"title": "IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning",
"abstract": ": 3D models of intracranial aneurysm segments with segmentation annotation in our dataset. Hot pink shows the healthy blood vessel part, and aqua shows the aneurysm part for each model. Medicine is an important application area for... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1902.10840 | 1512.03012 | NRSf M on CMU Motion Capture | One can see that our method gets far more precise reconstructions even when adding up #REFR Paladini et al. | [
"We randomly select a frame for each subject and render the reconstructed human skeleton in Figure 5 (a) to 5 (j).",
"To give a sense of the quality of reconstructions when our method fails, we go through all ten subjects in a total of 140,606 frames and select the frames with the largest errors as shown in Figur... | [
"#OTHEREFR fails on all sequences and therefore removed from the table.",
"Works #OTHEREFR did not release code.",
"Works #OTHEREFR Mean point distance (cm) to 20% noise to our image coordinates compared to baselines with no noise perturbation.",
"This experiment clearly demonstrates the robustness of our mod... | [
"precise reconstructions"
] | method | {
"title": "Deep Interpretable Non-Rigid Structure from Motion",
"abstract": "All current non-rigid structure from motion (NRSfM) algorithms are limited with respect to: (i) the number of images, and (ii) the type of shape variability they can handle. This has hampered the practical utility of NRSfM for many applic... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1801.03399 | 1512.03012 | 2D and 3D Keypoint Localization | For data synthesis, we sample CAD models of 472 cars, 100 sofas, 100 chairs and 62 beds from ShapeNet #REFR . | [
"In this Section, we demonstrate the performance of the deep supervision network (Fig.",
"4) for predicting the locations of object keypoints on 2D image and 3D space. Dataset."
] | [
"Each car model is annotated with 36 keypoints #OTHEREFR and each furniture model (chair, sofa or bed) with 14 keypoints #OTHEREFR .",
"#OTHEREFR We synthesize 600 k car images including occluded instances and 300 k images of fully visible furniture (chair+sofa+bed).",
"We pick rendered images of 5 CAD models f... | [
"ShapeNet"
] | method | {
"title": "Deep Supervision with Intermediate Concepts",
"abstract": "Recent data-driven approaches to scene interpretation predominantly pose inference as an end-to-end black-box mapping, commonly performed by a Convolutional Neural Network (CNN). However, decades of work on perceptual organization in both human ... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1811.12016 | 1512.03012 | Dataset | We consider the ShapeNet dataset #REFR which contains a rich collection of 3D CAD models, and is widely used in recent research works related to 2D/3D data. | [] | [
"Three categories, airplane, car, and chair, are selected for our experiments. For fair comparisons, we consider two different data settings.",
"For supervised learning of our model and to perform comparisons, we follow the works of 3D-R2N2 #OTHEREFR , Octree Generating Network (OGN) #OTHEREFR , Point Set Generat... | [
"ShapeNet"
] | method | {
"title": "3D Shape Reconstruction from a Single 2D Image via 2D-3D Self-Consistency",
"abstract": "Aiming at inferring 3D shapes from 2D images, 3D shape reconstruction has drawn huge attention from researchers in computer vision and deep learning communities. However, it is not practical to assume that 2D input ... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1811.01068 | 1512.03012 | Overview | Training: At training time, our method takes as input a class-specific collection of 3D shapes (we used ShapeN et #REFR ) for which part label annotations are available. | [
"In this section we provide a high level overview of our 3D Pick & Mix retrieval system.",
"Our system requires a training stage in which: (i) manifolds of 3D shapes of object parts are built (see Fig.",
"4 ) and (ii) a CNN is trained to take as input an image and regress the coordinates of each of its constitu... | [
"The first step at training time is to learn a separate shape manifold for each object part (see Fig. 4 ).",
"Each shape is represented with a Light Field descriptor #OTHEREFR and characterized with a pyramid of HoG features.",
"The manifolds are then built using non-linear multi-dimensional-scaling (MDS) and t... | [
"3D shapes"
] | method | {
"title": "3D Pick&Mix: Object Part Blending in Joint Shape and Image Manifolds",
"abstract": "Abstract. We present 3D Pick & Mix, a new 3D shape retrieval system that provides users with a new level of freedom to explore 3D shape and Internet image collections by introducing the ability to reason about objects at... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1809.10468 | 1512.03012 | IV. EXPERIMENTAL RESULTS | In the next section we evaluate the repeatability and accuracy of the corner detector on 3D models of washers from the ShapeNet dataset #REFR . | [
"In the first section of our results, we evaluate the proposed algorithm for edge detection against state-of-the-art edge detection algorithms for organized and unorganized point clouds.",
"We demonstrate our results on the RGB-D semantic segmentation dataset #OTHEREFR for comparison."
] | [
"Finally, we show how the algorithms proposed above can be used to automate welding of a panel workpiece.",
"All experiments described in the following sections are run on an Intel i7-4600M CPU with 2.9 GHz and 8GB RAM.",
"No multithreading or any other parallelism such as OpenMP or GPU was used in our implemen... | [
"ShapeNet"
] | method | {
"title": "Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Welding",
"abstract": "In this paper, we propose novel edge and corner detection algorithms for unorganized point clouds. Our edge detection method evaluates symmetry in a local neighborhood and uses an adaptive densit... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1612.00101 | 1512.03012 | Method Overview | At test time, we use the ShapeNet database #REFR as a powerful geometric prior, where we retrieve high-resolution geometry that respects the high-level structure of the previously obtained predictions. | [
"Specifically, we input the probability class vector of a 3D-CNN classification output into the latent space of the 3D-EPN.",
"Another important challenge on 3D shape completion is the high dimensionality; one of the insights here is that we use a (mostly) continuous distance field representation over an occupanc... | [
"We establish correlations between the low-resolution 3D-EPN output and the database geometry by learning a geometry lookup with volumetric features.",
"Here, we utilize the feature learning of volumetric convolutional networks with a modified version of Qi et et al.",
"#OTHEREFR whose learned features are the ... | [
"ShapeNet database"
] | method | {
"title": "Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis",
"abstract": "Our method completes a partial 3D scan using a 3D Encoder-Predictor network that leverages semantic features from a 3D classification network. The predictions are correlated with a shape database, which we use in a multi... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1807.02740 | 1512.03012 | Introduction | In our experiments, models from seven categories in ShapeNetCore #REFR are utilized in the training and testing process. | [
"In their recent work, they mention shape completion as one of the potential applications for their network.",
"Nevertheless, there is no further exploration in upsampling conditions and the categories of objects.",
"In this work, we build on and extend Achlioptas et al.'s work to deploy an upsampling method de... | [
"The results reveal that data-driven upsampling of sparse point clouds can indeed benefit significantly from categorical class information and moreover, the richness in the data (as obtained through multi-class training) results in high-quality upsampled models for a variety of object categories.",
"The key contr... | [
"models"
] | method | {
"title": "Data-driven Upsampling of Point Clouds",
"abstract": "High quality upsampling of sparse 3D point clouds is critically useful for a wide range of geometric operations such as reconstruction, rendering, meshing, and analysis. In this paper, we propose a data-driven algorithm that enables an upsampling of ... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1912.03663 | 1512.03012 | Results | The reconstruction task is evaluated with point sets of 2048 points, sampled from ShapeNet Core55 database #REFR . | [
"In this section, we present the results of our sampling approach for various applications: point cloud classification, retrieval, registration, and reconstruction.",
"The performance with point clouds sampled by our method is contrasted with the commonly used FPS and the learned sampling method, S-NET, proposed ... | [
"We use four shape classes with the largest number of examples: table, car, chair, and airplane. Each class is split to 85%/5%/10% for train/validation/test sets.",
"Our sampling network SampleNet is based on PointNet architecture.",
"It operates directly on point clouds and is invariant to permutations of the ... | [
"ShapeNet Core55 database"
] | method | {
"title": "SampleNet: Differentiable Point Cloud Sampling",
"abstract": "There is a growing number of tasks that work directly on point clouds. As the size of the point cloud grows, so do the computational demands of these tasks. A possible solution is to sample the point cloud first. Classic sampling approaches, ... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1709.00849 | 1512.03012 | Comparision of CRF and Grab-Cut segmentation from bound box labels | We choose ShapeNet #REFR database since it provides a large variety of models in the 20 categories for PASCAL segmentation challenge. | [
"We use Cycles Render Engine available with Blender since it supports ray-tracing to render synthetic images.",
"Since all the required information for annotation is available, we use the PASCAL Segmentation label format with labelled pixels for 20 classes.",
"Real world images have lot of information embedded ... | [
"Figure 3a shows few of the models used for rendering images.",
"The variety helps randomize the aspect of shape, texture and materials of the objects.",
"We use images from SUN database #OTHEREFR as background images.",
"From the large categories of images, we select few categories relevant as background to ... | [
"PASCAL segmentation challenge",
"ShapeNet database"
] | method | {
"title": "Dataset Augmentation with Synthetic Images Improves Semantic Segmentation",
"abstract": "Although Deep Convolutional Neural Networks trained with strong pixel-level annotations have significantly pushed the performance in semantic segmentation, annotation efforts required for the creation of training da... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1904.04094 | 1512.03012 | INTRODUCTION | The most obvious attempt for a 3D ImageNet comes in the form of ShapeNet #REFR . | [
"It is immediately clear that the number of training samples becomes an issue.",
"However, the success of 2D deep learning is often largely accredited to the release of large open-access labelled datasets such as ImageNet #OTHEREFR , which contains > 14 * 10 6 images.",
"It is now largely standard procedure to ... | [
"ShapeNet contains over 300 million models with 220,000 classified into 3,135 classes arranged using WordNet hypernym-hyponym relationships.",
"Similarly, ScanNet #OTHEREFR contains over 1500 indoor scene scans, with each scan containing 400-600k points.",
"With respect to outdoor point cloud processing there h... | [
"3D ImageNet",
"ShapeNet"
] | background | {
"title": "Weighted Point Cloud Augmentation for Neural Network Training Data Class-Imbalance",
"abstract": "Recent developments in the field of deep learning for 3D data have demonstrated promising potential for end-to-end learning directly from point clouds. However, many real-world point clouds contain a large ... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1807.06010 | 1512.03012 | Experiments | To perform the experiments presented in this section, we do not reuse the models in the training data set but download additional 3D models from ShapeNet #REFR . | [
"Dataset overview.",
"Since most models in #OTHEREFR are manifolds without sharp edges, we collected 24 CAD models and 12 everyday objects as our training data set, and manually annotate sharp edges on them; see supplemental material.",
"Then, we randomly crop 2,400 patches from the models (see Figure 2 ) to tr... | [
"For each testing model, we also use the procedure in Sec.",
"2.1 to generate the virtual scanned point clouds as input."
] | [
"ShapeNet"
] | method | {
"title": "EC-Net: an Edge-aware Point set Consolidation Network",
"abstract": "Abstract. Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of ... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1812.03441 | 1512.03012 | Overview of user studies | When a piece of furniture was mismatched, it was replaced with another similar object of the same type taken from the ShapeNet database #REFR . | [
"• Furniture quality reduced.",
"For both the couch and the stuffed chair, models were generated that contained 10%, 25%, 50%, and 75% of the original number of vertices.",
"If \"decimated furniture\" was one of the errors in a given room, the sofa and the stuffed chair always varied together.",
"• Furniture ... | [
"• Furniture repositioned.",
"Furniture objects were either globally raised by 10cm, globally lowered by 10cm, or globally moved outward (away from room center) by 10%.",
"The corresponding \"moved inward by 10% condition\" was not tested due to experimenter error.",
"• Furniture rescaled.",
"One of the fol... | [
"furniture",
"ShapeNet database"
] | method | {
"title": "Virtual replicas of real places: Experimental investigations",
"abstract": "The emergence of social virtual reality (VR) experiences, such as Facebook Spaces, Oculus Rooms, and Oculus Venues, will generate increased interest from users who want to share real places (both personal and public) with their ... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1804.10975 | 1512.03012 | Prior Work | With the advent of large-scale shape collections #REFR , data-driven methods, and especially CNNs, have become the method of choice for predicting 3D shapes. | [] | [
"Insprired by the success of CNNs for dense 2D prediction tasks, Wu et al. #OTHEREFR adapted CNNs to volumetric outputs. Yan et al. #OTHEREFR and Zhu et al.",
"#OTHEREFR showed that optimizing projections of the predicted shape benefits the reconstruction. Choy et al.",
"#OTHEREFR developed a joint approach for... | [
"large-scale shape collections"
] | method | {
"title": "Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers",
"abstract": ". Exemplary shape reconstructions from a single image by our Matryoshka network based on nested shape layers. In"
} | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1904.08921 | 1512.03012 | Introduction | With surface primitives in place of curves, we perform volumetric abstraction on ShapeNet #REFR , inputting an image or a distance field and outputting parametric primitives that approximate the model. | [
"We apply our new framework in the 2D context to a diverse dataset of fonts.",
"We train a network that takes in a raster image of a glyph and outputs a representation as a collection of Bézier curves.",
"This maps glyphs onto a common set of parameters that can be traversed intuitively.",
"We use this embedd... | [
"This output can be rendered at any resolution or converted to a mesh; it also can be used for segmentation. Contributions.",
"We present a technique for predicting parametric shapes from 2D and 3D raster data, including:",
"• a general distance field loss function allowing definition of several losses based on... | [
"ShapeNet"
] | method | {
"title": "Deep Parametric Shape Predictions using Distance Fields",
"abstract": "Many tasks in graphics and vision demand machinery for converting shapes into representations with sparse sets of parameters; these representations facilitate rendering, editing, and storage. When the source data is noisy or ambiguou... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
2003.10983 | 1512.03012 | Object Reconstruction | ShapeNet #REFR We quantitatively evaluate surface reconstruction accuracy of DeepLS and other shape learning methods on various classes from the ShapeNet dataset. | [] | [
"Quantitative results for the chamfer distance error are shown in Table 1 .",
"As can be seen DeepLS improves over related approaches by approximately one order of magnitude.",
"It should be noted that this is not a comparison between equal methods since the other methods infer a global, object-level representa... | [
"ShapeNet dataset"
] | method | {
"title": "Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction",
"abstract": ": Reconstruction performed by our Deep Local Shapes (DeepLS) of the Burghers of Calais scene [56] . DeepLS represents surface geometry as a sparse set of local latent codes in a voxel grid, as shown on the right. ... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1911.06971 | 1512.03012 | Auto-encoding 2D shapes | The order of the three shapes is sorted so that the diamond is always on the left and the hollow diamond is always on the right -this is to mimic the structure of shape datasets such as ShapeNet #REFR . | [
"To illustrate how our network works, we created a synthetic 2D dataset.",
"We place a diamond, a cross, and a hollow diamond with varying sizes over 64 × 64 images; see Figure 6 : Segmentation and correspondence -Semantics implied from autoencoding by BSP-Net.",
"Colors shown here are the result of a manual gr... | [
"After training Stage 1, our network has already achieved a good approximate S + reconstruction, however, by inspecting S * , the output of our inference, we can see there are several imperfections.",
"After the fine-tuning in Stage 2, our network achieves near perfect reconstructions.",
"Finally, the use of ov... | [
"ShapeNet"
] | method | {
"title": "BSP-Net: Generating Compact Meshes via Binary Space Partitioning",
"abstract": "Polygonal meshes are ubiquitous in the digital 3D domain, yet they have only played a minor role in the deep learning revolution. Leading methods for learning generative models of shapes rely on implicit functions, and gener... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
1904.05767 | 1512.03012 | Related work | Unlike existing methods that typically input simple renderings of CAD models, such as ShapeNet #REFR , we work with complex images in the presence of hand occlusions. | [
"We employ the latter since meshes allow better modeling of the interaction with the hand.",
"AtlasNet #OTHEREFR inputs vertex coordinates concatenated with image features and outputs a deformed mesh.",
"More recently, Pixel2Mesh #OTHEREFR explores regularizations to improve the perceptual quality of predicted ... | [
"In-hand scanning #OTHEREFR , while performed in the context of manipulation, focuses on object reconstruction and requires RGB-D video inputs. Hand-object reconstruction.",
"Joint reconstruction of hands and objects has been studied with multi-view RGB #OTHEREFR and RGB-D input with either optimization #OTHEREFR... | [
"ShapeNet"
] | method | {
"title": "Learning Joint Reconstruction of Hands and Manipulated Objects",
"abstract": "Estimating hand-object manipulations is essential for in- terpreting and imitating human actions. Previous work has made significant progress towards reconstruction of hand poses and object shapes in isolation. Yet, reconstruc... | {
"title": "ShapeNet: An Information-Rich 3D Model Repository",
"abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a c... |
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