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We present a simple and efficient method based on deep learning to automatically decompose sketched objects into semantically valid parts. We train a deep neural network to transfer existing segmentations and labelings from 3D models to freehand sketches without requiring numerous well-annotated sketches as training data. The network takes the binary image of a sketched object as input and produces a corresponding segmentation map with per-pixel labelings as output. A subsequent post-process procedure with multi-label graph cuts further refines the segmentation and labeling result. We validate our proposed method on two sketch datasets. Experiments show that our method outperforms the state-of-the-art method in terms of segmentation and labeling accuracy and is significantly faster, enabling further integration in interactive drawing systems. We demonstrate the efficiency of our method in a sketch-based modeling application that automatically transforms input sketches into 3D models by part assembly. | Fast Sketch Segmentation and Labeling with Deep Learning | 10,200 |
Procedural content generation (PCG) concerns all sorts of algorithms and tools which automatically produce game content, without requiring manual authoring by game artists. Besides generating com-plex static meshes, the PCG core usually encompasses geometrical information about the game world that can be useful in supporting other critical subsystems of the game engine. We discuss our experi-ence from the development of the iOS game title named "Fallen God: Escape Underworld", and show how our PCG produced extra metadata regarding the game world, in particular: (i) an annotated dun-geon graph to support path finding for NPC AI to attack or avoid the player (working for bipeds, birds, insects and serpents); (ii) a quantized voxel space to allow discrete A* for the dynamic camera system to work in the continuous 3d space; (iii) dungeon portals to support a dynamic PVS; and (iv) procedural ambient occlusion and tessellation of a separate set of simplified meshes to support very-fast and high-quality light mapping. | There is more to PCG than Meets the Eye: NPC AI, Dynamic Camera, PVS and
Lightmaps | 10,201 |
We report results from a preliminary study exploring the memorability of spatial scientific visualizations, the goal of which is to understand the visual features that contribute to memorability. The evaluation metrics include three objective measures (entropy, feature congestion, the number of edges), four subjective ratings (clutter, the number of distinct colors, familiarity, and realism), and two sentiment ratings (interestingness and happiness). We curate 1142 scientific visualization (SciVis) images from the original 2231 images in published IEEE SciVis papers from 2008 to 2017 and compute memorability scores of 228 SciVis images from data collected on Amazon Mechanical Turk (MTurk). Results showed that the memorability of SciVis images is mostly correlated with clutter and the number of distinct colors. We further investigate the differences between scientific visualization and infographics as a means to understand memorability differences by data attributes. | Toward A Deep Understanding of What Makes a Scientific Visualization
Memorable | 10,202 |
In this paper, we present an approach to enhance and improve the current normal map rendering technique. Our algorithm is based on semi-discrete Optimal Mass Transportation (OMT) theory and has a solid theoretical base. The key difference from previous normal map method is that we preserve the local area when we unwrap a disk-like 3D surface onto 2D plane. Compared to the currently used techniques which is based on conformal parameterization, our method does not need to cut a surface into many small pieces to avoid the large area distortion. The following charts packing step is also unnecessary in our framework. Our method is practical and makes the normal map technique more robust and efficient. | The Normal Map Based on Area-Preserving Parameterization | 10,203 |
We have entered the era of large multidimensional datasets represented by increasingly complex data structures. Current tools for scientific visualization are not optimized to efficiently and intuitively create cinematic production quality, time-evolving representations of numerical data for broad impact science communication via film, media, or journalism. To present such data in a cinematic environment, it is advantageous to develop methods that integrate these complex data structures into industry standard visual effects software packages, which provide a myriad of control features otherwise unavailable in traditional scientific visualization software. In this paper, we present the general methodology for the import and visualization of nested multiresolution datasets into commercially available visual effects software. We further provide a specific example of importing Adaptive Mesh Refinement data into the software Houdini. This paper builds on our previous work, which describes a method for using Houdini to visualize uniform Cartesian datasets. We summarize a tutorial available on the website www.ytini.com, which includes sample data downloads, Python code, and various other resources to simplify the process of importing and rendering multiresolution data. | Cinematic Visualization of Multiresolution Data: Ytini for Adaptive Mesh
Refinement in Houdini | 10,204 |
We present SAGNet, a structure-aware generative model for 3D shapes. Given a set of segmented objects of a certain class, the geometry of their parts and the pairwise relationships between them (the structure) are jointly learned and embedded in a latent space by an autoencoder. The encoder intertwines the geometry and structure features into a single latent code, while the decoder disentangles the features and reconstructs the geometry and structure of the 3D model. Our autoencoder consists of two branches, one for the structure and one for the geometry. The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code. This explicit intertwining of information enables separately controlling the geometry and the structure of the generated models. We evaluate the performance of our method and conduct an ablation study. We explicitly show that encoding of shapes accounts for both similarities in structure and geometry. A variety of quality results generated by SAGNet are presented. The data and code are at https://github.com/zhijieW-94/SAGNet. | SAGNet:Structure-aware Generative Network for 3D-Shape Modeling | 10,205 |
In view of the problem of image inpainting error continuation and the deviation of finding best match block, an improved Criminisi algorithm is proposed. The improvement was mainly embodied in two aspects. In the repairing order aspect, we redefine the calculation formula of the priority. In order to solve the problem of error continuation caused by local confidence item updating, the mean value of Manhattan distance is used for replace the confidence item. In the matching strategy aspect, finding the best match block not only depend on the difference of the two pixels, but also consider the matching region. Therefore, Euclidean distance is introduced. Experiments confirm that the improved algorithm can overcome the insufficiencies of the original algorithm. The repairing effect has been improved, and the results have a better visual appearance. | Image Inpainting Based on a Novel Criminisi Algorithm | 10,206 |
The accuracy and fidelity of deformation simulations are highly dependent upon the underlying constitutive material model. Commonly used linear or nonlinear constitutive material models only cover a tiny part of possible material behavior. In this work we propose a unified framework for modeling deformable material. The key idea is to use a neural network to correct a nominal model of the elastic and damping properties of the object. The neural network encapsulates a complex function that is hard to explicitly model. It injects force corrections that help the forward simulation to more accurately predict the true behavior of a given soft object, which includes non-linear elastic forces and damping. Attempting to satisfy the requirement from real material interference and animation design scenarios, we learn material models from examples of dynamic behavior of a deformable object's surface. The challenge is that such data is sparse as it is consistently given only on part of the surface. Sparse reduced space-time optimization is employed to gradually generate increasingly accurate training data, which further refines and enhances the neural network. We evaluate our choice of network architecture and show evidence that the modest amount of training data we use is suitable for the problem tackled. Our method is demonstrated with a set of synthetic examples. | Neural Material: Learning Elastic Constitutive Material and Damping
Models from Sparse Data | 10,207 |
This article describes the capabilities of a universal software platform for visualizing class F curves and developing specialized applications for CAD systems based on Microsoft Excel VBA, the software complex FairCurveModeler, and computer algebra systems. Additionally, it demonstrates the use of a software platform for visualizing functional and log-aesthetic curves integrated with CAD Fusion360. The value of the curves is evident in visualizing the qualitative geometry of the product shape in industrial design. Moreover, the requirements for the characteristics of class F curves are emphasized to form a visual purity of shape in industrial design and to provide a positive emotional perception of the visual image of the product by a person. | Universal software platform for visualizing class F curves,
log-aesthetic curves and development of applied CAD systems | 10,208 |
We propose the concept of intelligent middle-level game control, which lies on a continuum of control abstraction levels between the following two dual opposites: 1) high-level control that translates player's simple commands into complex actions (such as pressing Space key for jumping), and 2) low-level control which simulates real-life complexities by directly manipulating, e.g., joint rotations of the character as it is done in the runner game QWOP. We posit that various novel control abstractions can be explored using recent advances in movement intelligence of game characters. We demonstrate this through design and evaluation of a novel 2-player martial arts game prototype. In this game, each player guides a simulated humanoid character by clicking and dragging body parts. This defines the cost function for an online continuous control algorithm that executes the requested movement. Our control algorithm uses Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in a rolling horizon manner with custom population seeding techniques. Our playtesting data indicates that intelligent middle-level control results in producing novel and innovative gameplay without frustrating interface complexities. | Intelligent Middle-Level Game Control | 10,209 |
BRDF models are ubiquitous tools for the representation of material appearance. However, there is now an astonishingly large number of different models in practical use. Both a lack of BRDF model standardisation across implementations found in different renderers, as well as the often semantically different capabilities of various models, have grown to be a major hindrance to the interchange of production assets between different rendering systems. Current attempts to solve this problem rely on manually finding visual similarities between models, or mathematical ones between their functional shapes, which requires access to the shader implementation, usually unavailable in commercial renderers. We present a method for automatic translation of material appearance between different BRDF models, which uses an image-based metric for appearance comparison, and that delegates the interaction with the model to the renderer. We analyse the performance of the method, both with respect to robustness and visual differences of the fits for multiple combinations of BRDF models. While it is effective for individual BRDFs, the computational cost does not scale well for spatially-varying BRDFs. Therefore, we further present a parametric regression scheme that approximates the shape of the transformation function and generates a reduced representation which evaluates instantly and without further interaction with the renderer. We present respective visual comparisons of the remapped SVBRDF models for commonly used renderers and shading models, and show that our approach is able to extrapolate transformed BRDF parameters better than other complex regression schemes. | Image-based remapping of spatially-varying material appearance | 10,210 |
General image completion and extrapolation methods often fail on portrait images where parts of the human body need to be recovered - a task that requires accurate human body structure and appearance synthesis. We present a two-stage deep learning framework for tacking this problem. In the first stage, given a portrait image with an incomplete human body, we extract a complete, coherent human body structure through a human parsing network, which focuses on structure recovery inside the unknown region with the help of pose estimation. In the second stage, we use an image completion network to fill the unknown region, guided by the structure map recovered in the first stage. For realistic synthesis the completion network is trained with both perceptual loss and conditional adversarial loss. We evaluate our method on public portrait image datasets, and show that it outperforms other state-of-art general image completion methods. Our method enables new portrait image editing applications such as occlusion removal and portrait extrapolation. We further show that the proposed general learning framework can be applied to other types of images, e.g. animal images. | Deep Portrait Image Completion and Extrapolation | 10,211 |
We reconstruct a closed denoised curve from an unstructured and highly noisy 2D point cloud. Our proposed method uses a two- pass approach: Previously recovered manifold connectivity is used for ordering noisy samples along this manifold and express these as residuals in order to enable parametric denoising. This separates recovering low-frequency features from denoising high frequencies, which avoids over-smoothing. The noise probability density functions (PDFs) at samples are either taken from sensor noise models or from estimates of the connectivity recovered in the first pass. The output curve balances the signed distances (inside/outside) to the samples. Additionally, the angles between edges of the polygon representing the connectivity become minimized in the least-square sense. The movement of the polygon's vertices is restricted to their noise extent, i.e., a cut-off distance corresponding to a maximum variance of the PDFs. We approximate the resulting optimization model, which consists of higher-order functions, by a linear model with good correspondence. Our algorithm is parameter-free and operates fast on the local neighborhoods determined by the connectivity. We augment a least-squares solver constrained by a linear system to also handle bounds. This enables us to guarantee stochastic error bounds for sampled curves corrupted by noise, e.g., silhouettes from sensed data, and we improve on the reconstruction error from ground truth. Open source to reproduce figures and tables in this paper is available at: https://github.com/stefango74/stretchdenoise | StretchDenoise: Parametric Curve Reconstruction with Guarantees by
Separating Connectivity from Residual Uncertainty of Samples | 10,212 |
We present a convolutional neural network (CNN) based solution for modeling physically plausible spatially varying surface reflectance functions (SVBRDF) from a single photograph of a planar material sample under unknown natural illumination. Gathering a sufficiently large set of labeled training pairs consisting of photographs of SVBRDF samples and corresponding reflectance parameters, is a difficult and arduous process. To reduce the amount of required labeled training data, we propose to leverage the appearance information embedded in unlabeled images of spatially varying materials to self-augment the training process. Starting from an initial approximative network obtained from a small set of labeled training pairs, we estimate provisional model parameters for each unlabeled training exemplar. Given this provisional reflectance estimate, we then synthesize a novel temporary labeled training pair by rendering the exact corresponding image under a new lighting condition. After refining the network using these additional training samples, we re-estimate the provisional model parameters for the unlabeled data and repeat the self-augmentation process until convergence. We demonstrate the efficacy of the proposed network structure on spatially varying wood, metals, and plastics, as well as thoroughly validate the effectiveness of the self-augmentation training process. | Modeling Surface Appearance from a Single Photograph using
Self-augmented Convolutional Neural Networks | 10,213 |
The Mandelbox is a recently discovered class of escape-time fractals which use a conditional combination of reflection, spherical inversion, scaling, and translation to transform points under iteration. In this paper we introduce a new extension to Mandelbox fractals which replaces spherical inversion with a more generalized shape inversion. We then explore how this technique can be used to generate new fractals in 2D, 3D, and 4D. | Extending Mandelbox Fractals with Shape Inversions | 10,214 |
Using (casual) images to texture 3D models is a common way to create realistic 3D models, which is a very important task in computer graphics. However, if the shape of the casual image does not look like the target model or the target mapping area, the textured model will become strange since the image will be distorted very much. In this paper, we present a novel texturing and deforming approach for mapping the pattern and shape of a casual image to a 3D model at the same time based on an alternating least-square approach. Through a photogrammetric method, we project the target model onto the source image according to the estimated camera model. Then, the target model is deformed according to the shape of the source image using a surface-based deformation method while minimizing the image distortion simultaneously. The processes are performed iteratively until convergence. Hence, our method can achieve texture mapping, shape deformation, and detail-preserving at once, and can obtain more reasonable texture mapped results than traditional methods. | Texturing and Deforming Meshes with Casual Images | 10,215 |
In recent years, mesh subdivision---the process of forging smooth free-form surfaces from coarse polygonal meshes---has become an indispensable production instrument. Although subdivision performance is crucial during simulation, animation and rendering, state-of-the-art approaches still rely on serial implementations for complex parts of the subdivision process. Therefore, they often fail to harness the power of modern parallel devices, like the graphics processing unit (GPU), for large parts of the algorithm and must resort to time-consuming serial preprocessing. In this paper, we show that a complete parallelization of the subdivision process for modern architectures is possible. Building on sparse matrix linear algebra, we show how to structure the complete subdivision process into a sequence of algebra operations. By restructuring and grouping these operations, we adapt the process for different use cases, such as regular subdivision of dynamic meshes, uniform subdivision for immutable topology, and feature-adaptive subdivision for efficient rendering of animated models. As the same machinery is used for all use cases, identical subdivision results are achieved in all parts of the production pipeline. As a second contribution, we show how these linear algebra formulations can effectively be translated into efficient GPU kernels. Applying our strategies to $\sqrt{3}$, Loop and Catmull-Clark subdivision shows significant speedups of our approach compared to state-of-the-art solutions, while we completely avoid serial preprocessing. | AlSub: Fully Parallel and Modular Subdivision | 10,216 |
In this paper we present two new ideas for generating star patterns and filling the gaps during the tile operation. Firstly, we introduce a novel parametric method based on concentric circles for generating stars and rosettes. Using proposed method, completely different stars and rosettes and a set of new and complex star patterns convert to each other only by changing nine parameters. Secondly, we demonstrate how three equal tangent circles can be used as a base for generating tile elements. For this reason a surrounded circle is created among tangent circles, which represents the gaps in hexagonal packing. Afterwards, we use our first idea for filling the tangent circles and surrounded circle. This parametric approach can be used for generating infinite new star patterns, which some of them will be presented in result section.Two Android apps of proposed method called Starking and Tilerking are available in Google app store. | Next Generation of Star Patterns | 10,217 |
Synthesizing 3D faces that give certain personality impressions is commonly needed in computer games, animations, and virtual world applications for producing realistic virtual characters. In this paper, we propose a novel approach to synthesize 3D faces based on personality impression for creating virtual characters. Our approach consists of two major steps. In the first step, we train classifiers using deep convolutional neural networks on a dataset of images with personality impression annotations, which are capable of predicting the personality impression of a face. In the second step, given a 3D face and a desired personality impression type as user inputs, our approach optimizes the facial details against the trained classifiers, so as to synthesize a face which gives the desired personality impression. We demonstrate our approach for synthesizing 3D faces giving desired personality impressions on a variety of 3D face models. Perceptual studies show that the perceived personality impressions of the synthesized faces agree with the target personality impressions specified for synthesizing the faces. Please refer to the supplementary materials for all results. | 3D Face Synthesis Driven by Personality Impression | 10,218 |
The arrangement of objects into a layout can be challenging for non-experts, as is affirmed by the existence of interior design professionals. Recent research into the automation of this task has yielded methods that can synthesize layouts of objects respecting aesthetic and functional constraints that are non-linear and competing. These methods usually adopt a stochastic optimization scheme, which samples from different layout configurations, a process that is slow and inefficient. We introduce an physics-motivated, continuous layout synthesis technique, which results in a significant gain in speed and is readily scalable. We demonstrate our method on a variety of examples and show that it achieves results similar to conventional layout synthesis based on Markov chain Monte Carlo (McMC) state-search, but is faster by at least an order of magnitude and can handle layouts of unprecedented size as well as tightly-packed layouts that can overwhelm McMC. | Fast and Scalable Position-Based Layout Synthesis | 10,219 |
We consider the tasks of representing, analyzing and manipulating maps between shapes. We model maps as densities over the product manifold of the input shapes; these densities can be treated as scalar functions and therefore are manipulable using the language of signal processing on manifolds. Being a manifold itself, the product space endows the set of maps with a geometry of its own, which we exploit to define map operations in the spectral domain; we also derive relationships with other existing representations (soft maps and functional maps). To apply these ideas in practice, we discretize product manifolds and their Laplace--Beltrami operators, and we introduce localized spectral analysis of the product manifold as a novel tool for map processing. Our framework applies to maps defined between and across 2D and 3D shapes without requiring special adjustment, and it can be implemented efficiently with simple operations on sparse matrices. | Functional Maps Representation on Product Manifolds | 10,220 |
We present a data-driven algorithm for generating gaits of virtual characters with varying dominance traits. Our formulation utilizes a user study to establish a data-driven dominance mapping between gaits and dominance labels. We use our dominance mapping to generate walking gaits for virtual characters that exhibit a variety of dominance traits while interacting with the user. Furthermore, we extract gait features based on known criteria in visual perception and psychology literature that can be used to identify the dominance levels of any walking gait. We validate our mapping and the perceived dominance traits by a second user study in an immersive virtual environment. Our gait dominance classification algorithm can classify the dominance traits of gaits with ~73% accuracy. We also present an application of our approach that simulates interpersonal relationships between virtual characters. To the best of our knowledge, ours is the first practical approach to classifying gait dominance and generate dominance traits in virtual characters. | Modeling Data-Driven Dominance Traits for Virtual Characters using Gait
Analysis | 10,221 |
We propose a collaborative 3D modeling system, which is based on the blockchain technology. Our approach uses the blockchain to communicate with modeling tools and to provide them a decentralized database of the mesh modification history. This approach also provides a server-less version control system: users can commit their modifications to the blockchain and checkout others' modifications from the blockchain. As a result, our system enables users to do collaborative modeling without any central server. | Collaborative 3D modeling system based on blockchain | 10,222 |
Hand-drawn objects usually consist of multiple semantically meaningful parts. For example, a stick figure consists of a head, a torso, and pairs of legs and arms. Efficient and accurate identification of these subparts promises to significantly improve algorithms for stylization, deformation, morphing and animation of 2D drawings. In this paper, we propose a neural network model that segments symbols into stroke-level components. Our segmentation framework has two main elements: a fixed feature extractor and a Multilayer Perceptron (MLP) network that identifies a component based on the feature. As the feature extractor we utilize an encoder of a stroke-rnn, which is our newly proposed generative Variational Auto-Encoder (VAE) model that reconstructs symbols on a stroke by stroke basis. Experiments show that a single encoder could be reused for segmenting multiple categories of sketched symbols with negligible effects on segmentation accuracies. Our segmentation scores surpass existing methodologies on an available small state of the art dataset. Moreover, extensive evaluations on our newly annotated big dataset demonstrate that our framework obtains significantly better accuracies as compared to baseline models. We release the dataset to the community. | Stroke-based sketched symbol reconstruction and segmentation | 10,223 |
360{\deg} video provides an immersive experience for viewers, allowing them to freely explore the world by turning their head. However, creating high-quality 360{\deg} video content can be challenging, as viewers may miss important events by looking in the wrong direction, or they may see things that ruin the immersion, such as stitching artifacts and the film crew. We take advantage of the fact that not all directions are equally likely to be observed; most viewers are more likely to see content located at ``true north'', i.e. in front of them, due to ergonomic constraints. We therefore propose 360{\deg} video direction, where the video is jointly optimized to orient important events to the front of the viewer and visual clutter behind them, while producing smooth camera motion. Unlike traditional video, viewers can still explore the space as desired, but with the knowledge that the most important content is likely to be in front of them. Constraints can be user guided, either added directly on the equirectangular projection or by recording ``guidance'' viewing directions while watching the video in a VR headset, or automatically computed, such as via visual saliency or forward motion direction. To accomplish this, we propose a new motion estimation technique specifically designed for 360{\deg} video which outperforms the commonly used 5-point algorithm on wide angle video. We additionally formulate the direction problem as an optimization where a novel parametrization of spherical warping allows us to correct for some degree of parallax effects. We compare our approach to recent methods that address stabilization-only and converting 360{\deg} video to narrow field-of-view video. | Joint Stabilization and Direction of 360°Videos | 10,224 |
We present a novel approach to learning a point-wise, meaningful embedding for point-clouds in an unsupervised manner, through the use of neural-networks. The domain of point-cloud processing via neural-networks is rapidly evolving, with novel architectures and applications frequently emerging. Within this field of research, the availability and plethora of unlabeled point-clouds as well as their possible applications make finding ways of characterizing this type of data appealing. Though significant advancement was achieved in the realm of unsupervised learning, its adaptation to the point-cloud representation is not trivial. Previous research focuses on the embedding of entire point-clouds representing an object in a meaningful manner. We present a deep learning framework to learn point-wise description from a set of shapes without supervision. Our approach leverages self-supervision to define a relevant loss function to learn rich per-point features. We train a neural-network with objectives based on context derived directly from the raw data, with no added annotation. We use local structures of point-clouds to incorporate geometric information into each point's latent representation. In addition to using local geometric information, we encourage adjacent points to have similar representations and vice-versa, creating a smoother, more descriptive representation. We demonstrate the ability of our method to capture meaningful point-wise features through three applications. By clustering the learned embedding space, we perform unsupervised part-segmentation on point clouds. By calculating euclidean distance in the latent space we derive semantic point-analogies. Finally, by retrieving nearest-neighbors in our learned latent space we present meaningful point-correspondence within and among point-clouds. | PointWise: An Unsupervised Point-wise Feature Learning Network | 10,225 |
Affine transformation, layer blending, and artistic filters are popular processes that graphic designers employ to transform pixels of an image to create a desired effect. Here, we examine various approaches that synthesize new images: pixel-based compositing models and in particular, distributed representations of deep neural network models. This paper focuses on synthesizing new images from a learned representation model obtained from the VGG network. This approach offers an interesting creative process from its distributed representation of information in hidden layers of a deep VGG network i.e., information such as contour, shape, etc. are effectively captured in hidden layers of neural networks. Conceptually, if $\Phi$ is the function that transforms input pixels into distributed representations of VGG layers ${\bf h}$, a new synthesized image $X$ can be generated from its inverse function, $X = \Phi^{-1}({\bf h})$. We describe the concept behind the approach, present some representative synthesized images and style-transferred image examples. | Image Synthesis and Style Transfer | 10,226 |
Trusses are load-carrying light-weight structures consisting of bars connected at joints ubiquitously applied in a variety of engineering scenarios. Designing optimal trusses that satisfy functional specifications with a minimal amount of material has interested both theoreticians and practitioners for more than a century. In this paper, we introduce two main ideas to improve upon the state of the art. First, we formulate an alternating linear programming problem for geometry optimization. Second, we introduce two sets of complementary topological operations, including a novel subdivision scheme for global topology refinement inspired by Michell's famed theoretical study. Based on these two ideas, we build an efficient computational framework for the design of lightweight trusses. \AD{We illustrate our framework with a variety of functional specifications and extensions. We show that our method achieves trusses with smaller volumes and is over two orders of magnitude faster compared with recent state-of-the-art approaches. | Computational Design of Lightweight Trusses | 10,227 |
Orienting surface normals correctly and consistently is a fundamental problem in geometry processing. Applications such as visualization, feature detection, and geometry reconstruction often rely on the availability of correctly oriented normals. Many existing approaches for automatic orientation of normals on meshes or point clouds make severe assumptions on the input data or the topology of the underlying object which are not applicable to real-world measurements of urban scenes. In contrast, our approach is specifically tailored to the challenging case of unstructured indoor point cloud scans of multi-story, multi-room buildings. We evaluate the correctness and speed of our approach on multiple real-world point cloud datasets. | Automatic normal orientation in point clouds of building interiors | 10,228 |
Fast and realistic coupling of blood flow and vessel wall is of great importance to virtual surgery. In this paper, we propose a novel data-driven coupling method that formulates physics-based blood flow simulation as a regression problem, using an improved periodic-corrected neural network (PcNet), estimating the acceleration of every particle at each frame to obtain fast, stable and realistic simulation. We design a particle state feature vector based on smoothed particle hydrodynamics (SPH), modeling the mixed contribution of neighboring proxy particles on the blood vessel wall and neighboring blood particles, giving the extrapolation ability to deal with more complex couplings. We present a semi-supervised training strategy to improve the traditional BP neural network, which corrects the error periodically to ensure long term stability. Experimental results demonstrate that our method is able to implement stable and vivid coupling of blood flow and vessel wall while greatly improving computational efficiency. | Periodic-corrected data driven coupling of blood flow and vessel wall
for virtual surgery | 10,229 |
Given the spline representation of the boundary of a three dimensional domain, constructing a volumetric spline parameterization of the domain (i.e., a map from a unit cube to the domain) with the given boundary is a fundamental problem in isogeometric analysis. A good domain parameterization should satisfy the following criteria: (1) the parameterization is a bijective map; and (2) the map has lowest possible distortion. However, none of the state-of-the-art volumetric parameterization methods has fully addressed the above issues. In this paper, we propose a three-stage approach for constructing volumetric parameterization satisfying the above criteria. Firstly, a harmonic map is computed between a unit cube and the computational domain. Then a bijective map modeled by a max-min optimization problem is computed in a coarse-to-fine way, and an algorithm based on divide and conquer strategy is proposed to solve the optimization problem efficiently. Finally, to ensure high quality of the parameterization, the MIPS (Most Isometric Parameterizations) method is adopted to reduce the conformal distortion of the bijective map. We provide several examples to demonstrate the feasibility of our approach and to compare our approach with some state-of-the-art methods. The results show that our algorithm produces bijective parameterization with high quality even for complex domains. | Volumetric Spline Parameterization for Isogeometric Analysis | 10,230 |
We propose a novel volume conserving framework for character-water interaction, using a novel volume-of-fluid solver on a skinned tetrahedral mesh, enabling the high degree of the spatial adaptivity in order to capture thin films and hair-water interactions. For efficiency, the bulk of the fluid volume is simulated with a standard Eulerian solver which is two way coupled to our skinned arbitrary Lagrangian-Eulerian mesh using a fast, robust, and straightforward to implement partitioned approach. This allows for a specialized and efficient treatment of the volume-of-fluid solver, since it is only required in a subset of the domain. The combination of conservation of fluid volume and a kinematically deforming skinned mesh allows us to robustly implement interesting effects such as adhesion, and anisotropic porosity. We illustrate the efficacy of our method by simulating various water effects with solid objects and animated characters. | A Robust Volume Conserving Method for Character-Water Interaction | 10,231 |
Digital mosaics have usually used regular tiles, simulating the historical "tessellated" mosaics. In this paper, we present a method for synthesizing pebble mosaics, a historical mosaic style in which the tiles are rounded pebbles. We address both the tiling problem, where pebbles are distributed over the image plane so as to approximate the input image content, and the problem of geometry, creating a smooth rounded shape for each pebble. We adapt SLIC, simple linear iterative clustering, to obtain elongated tiles conforming to image content, and smooth the resulting irregular shapes into shapes resembling pebble cross-sections. Then, we create an interior and exterior contour for each pebble and solve a Laplace equation over the region between them to obtain height-field geometry. The resulting pebble set approximates the input image while presenting full geometry that can be rendered and textured for a highly detailed representation of a pebble mosaic. | Automated pebble mosaic stylization of images | 10,232 |
Urban planning often raises complex issues that are difficult to visualize and challenging to communicate. The increasing availability of 3D modeling standards has provided the opportunity for many developers, engineers, designers, planners, investors, and government officials to effectively collaborate to bring projects to fruition. Because of its real-time interactivity and widespread web-based content players, X3D proves to be a good choice for developing and visualizing 3D city content on the Web for planning purposes. Passenger rail is a viable and cost-effective transportation solution in many areas, especially in view of rising energy costs. The Savannah in 3D (or S3D) project is a multimedia tool for a feasibility study designed to bring passenger rail to Savannah; thereby opening up the historic, tourist-friendly city to a wider audience. The paper outlines the development process of an interactive 3D train model as it journeys from Atlanta to Savannah, Georgia - focusing on user interactivity and scene immersion to supplement the city and transportation planning agenda. | X3D in Urban Planning - Savannah in 3D | 10,233 |
Restricting path tracing to a small number of paths per pixel for performance reasons rarely achieves a satisfactory image quality for scenes of interest. However, path space filtering may dramatically improve the visual quality by sharing information across vertices of paths classified as proximate. Unlike screen space-based approaches, these paths neither need to be present on the screen, nor is filtering restricted to the first intersection with the scene. While searching proximate vertices had been more expensive than filtering in screen space, we greatly improve over this performance penalty by storing, updating, and looking up the required information in a hash table. The keys are constructed from jittered and quantized information, such that only a single query very likely replaces costly neighborhood searches. A massively parallel implementation of the algorithm is demonstrated on a graphics processing unit (GPU). | Massively Parallel Path Space Filtering | 10,234 |
In this paper, we provide a comprehensive theory of anti-aliasing sampling patterns that explains and revises known results, and show how patterns as predicted by the theory can be generated via a variational optimization framework. We start by deriving the exact spectral expression for expected error in reconstructing an image in terms of power spectra of sampling patterns, and analyzing how the shape of power spectra is related to anti-aliasing properties. Based on this analysis, we then formulate the problem of generating anti-aliasing sampling patterns as constrained variational optimization on power spectra. This allows us to not rely on any parametric form, and thus explore the whole space of realizable spectra. We show that the resulting optimized sampling patterns lead to reconstructions with less visible aliasing artifacts, while keeping low frequencies as clean as possible. | A Comprehensive Theory and Variational Framework for Anti-aliasing
Sampling Patterns | 10,235 |
We present a new motion-compensated hierarchical compression scheme (HMLFC) for encoding light field images (LFI) that is suitable for interactive rendering. Our method combines two different approaches, motion compensation schemes and hierarchical compression methods, to exploit redundancies in LFI. The motion compensation schemes capture the redundancies in local regions of the LFI efficiently (local coherence) and hierarchical schemes capture the redundancies present across the entire LFI (global coherence). Our hybrid approach combines the two schemes effectively capturing both local as well as global coherence to improve the overall compression rate. We compute a tree from LFI using a hierarchical scheme and use phase shifted motion compensation techniques at each level of the hierarchy. Our representation provides random access to the pixel values of the light field, which makes it suitable for interactive rendering applications using a small run-time memory footprint. Our approach is GPU friendly and allows parallel decoding of LF pixel values. We highlight the performance on the two-plane parameterized light fields and obtain a compression ratio of 30-800X with a PSNR of 40-45 dB. Overall, we observe a 2-5X improvement in compression rates using HMLFC over prior light field compression schemes that provide random access capability. In practice, our algorithm can render new views of resolution 512X512 on an NVIDIA GTX-980 at ~200 fps. | HMLFC: Hierarchical Motion-Compensated Light Field Compression for
Interactive Rendering | 10,236 |
A color appearance model (CAM) is an advanced colorimetric tool used to predict color appearance under a wide variety of viewing conditions. A chromatic adaptation transform (CAT) is an integral part of a CAM. Its role is to predict "corresponding colors," that is, a pair of colors that have the same color appearance when viewed under different illuminants, after partial or full adaptation to each illuminant. Modern CATs perform well when applied to a limited range of illuminant pairs and a limited range of source (test) colors. However, they can fail if operated outside these ranges. For imaging applications, it is important to have a CAT that can operate on any real color and illuminant pair without failure. This paper proposes a new CAT that does not operate on the standard von Kries model of adaptation. Instead it relies on spectral reconstruction and how these reconstructions behave with respect to different illuminants. It is demonstrated that the proposed CAT is immune to some of the limitations of existing CATs (such as producing colors with negative tristimulus values). The proposed CAT does not use established empirical corresponding-color datasets to optimize performance, as most modern CATs do, yet it performs as well as or better than the most recent CATs when tested against the corresponding-color datasets. This increase in robustness comes at the expense of additional complexity and computational effort. If robustness is of prime importance, then the proposed method may be justifiable. | Chromatic Adaptation Transform by Spectral Reconstruction (Preprint) | 10,237 |
The problem of decomposing non-manifold object has already been studied in solid modeling. However, the few proposed solutions are limited to the problem of decomposing solids described through their boundaries. In this thesis we study the problem of decomposing an arbitrary non-manifold simplicial complex into more regular components. A formal notion of decomposition is developed using combinatorial topology. The proposed decomposition is unique, for a given complex, and is computable for complexes of any dimension. A decomposition algorithm is proposed that is linear w.r.t. the size of the input. In three or higher dimensions a decomposition into manifold parts is not always possible. Thus, in higher dimensions, we decompose a non-manifold into a decidable super class of manifolds, that we call, Initial-Quasi-Manifolds. We also defined a two-layered data structure, the Extended Winged data structure. This data structure is a dimension independent data structure conceived to model non-manifolds through their decomposition into initial-quasi-manifold parts. Our two layered data structure describes the structure of the decomposition and each component separately. In the second layer we encode the connectivity structure of the decomposition. We analyze the space requirements of the Extended Winged data structure and give algorithms to build and navigate it. Finally, we discuss time requirements for the computation of topological relations and show that, for surfaces and tetrahedralizations, embedded in real 3D space, all topological relations can be extracted in optimal time. This approach offers a compact, dimension independent, representation for non-manifolds that can be useful whenever the modeled object has few non-manifold singularities. | Decomposition and Modeling in the Non-Manifold domain | 10,238 |
Purpose: In surgical navigation, pre-operative organ models are presented to surgeons during the intervention to help them in efficiently finding their target. In the case of soft tissue, these models need to be deformed and adapted to the current situation by using intra-operative sensor data. A promising method to realize this are real-time capable biomechanical models. Methods: We train a fully convolutional neural network to estimate a displacement field of all points inside an organ when given only the displacement of a part of the organ's surface. The network trains on entirely synthetic data of random organ-like meshes, which allows us to generate much more data than is otherwise available. The input and output data is discretized into a regular grid, allowing us to fully utilize the capabilities of convolutional operators and to train and infer in a highly parallelized manner. Results: The system is evaluated on in-silico liver models, phantom liver data and human in-vivo breathing data. We test the performance with varying material parameters, organ shapes and amount of visible surface. Even though the network is only trained on synthetic data, it adapts well to the various cases and gives a good estimation of the internal organ displacement. The inference runs at over 50 frames per second. Conclusions: We present a novel method for training a data-driven, real-time capable deformation model. The accuracy is comparable to other registration methods, it adapts very well to previously unseen organs and does not need to be re-trained for every patient. The high inferring speed makes this method useful for many applications such as surgical navigation and real-time simulation. | Learning Soft Tissue Behavior of Organs for Surgical Navigation with
Convolutional Neural Networks | 10,239 |
Estimation of the Discrete-Time Fourier Transform (DTFT) at points of a finite domain arises in many imaging applications. A new approach to this task, the Golden Angle Linogram Fourier Domain (GALFD), is presented, together with a computationally fast and accurate tool, named Golden Angle Linogram Evaluation (GALE), for approximating the DTFT at points of a GALFD. A GALFD resembles a Linogram Fourier Domain (LFD), which is efficient and accurate. A limitation of linograms is that embedding an LFD into a larger one requires many extra points, at least doubling the domain's cardinality. The GALFD, on the other hand, allows for incremental inclusion of relatively few data points. Approximation error bounds and floating point operations counts are presented to show that GALE computes accurately and efficiently the DTFT at the points of a GALFD. The ability to extend the data collection in small increments is beneficial in applications such as Magnetic Resonance Imaging. Experiments for simulated and for real-world data are presented to substantiate the theoretical claims. The mathematical analysis, algorithms, and software developed in the paper are equally suitable to other angular distributions of rays and therefore we bring the benefits of linograms to arbitrary radial patterns. | The Discrete Fourier Transform for Golden Angle Linogram Sampling | 10,240 |
We connect X3D to the state of the art OGRE renderer using our prototypical x3ogre implementation. At this we perform a comparison of both on a conceptual level, highlighting similarities and differences. Our implementation allows swapping X3D concepts for OGRE concepts and vice versa. We take advantage of this to analyse current shortcomings in X3D and propose X3D extensions to overcome those. | x3ogre: Connecting X3D to a state of the art rendering engine | 10,241 |
Parquetry is the art and craft of decorating a surface with a pattern of differently colored veneers of wood, stone or other materials. Traditionally, the process of designing and making parquetry has been driven by color, using the texture found in real wood only for stylization or as a decorative effect. Here, we introduce a computational pipeline that draws from the rich natural structure of strongly textured real-world veneers as a source of detail in order to approximate a target image as faithfully as possible using a manageable number of parts. This challenge is closely related to the established problems of patch-based image synthesis and stylization in some ways, but fundamentally different in others. Most importantly, the limited availability of resources (any piece of wood can only be used once) turns the relatively simple problem of finding the right piece for the target location into the combinatorial problem of finding optimal parts while avoiding resource collisions. We introduce an algorithm that allows to efficiently solve an approximation to the problem. It further addresses challenges like gamut mapping, feature characterization and the search for fabricable cuts. We demonstrate the effectiveness of the system by fabricating a selection of "photo-realistic" pieces of parquetry from different kinds of unstained wood veneer. | Computational Parquetry: Fabricated Style Transfer with Wood Pixels | 10,242 |
The construction of parametric curve and surface plays important role in computer aided geometric design (CAGD), computer aided design (CAD), and geometric modeling. In this paper, we define a new kind of blending functions associated with a real points set, called generalized toric-Bernstein (GT-Bernstein) basis functions. Then the generalized toric-Bezier (GT-B\'ezier) curves and surfaces are constructed based on the GT-Bernstein basis functions, which are the projections of the (irrational) toric varieties in fact and the generalizations of the classical rational B\'ezier curves and surfaces and toric surface patches. Furthermore, we also study the properties of the presented curves and surfaces, including the limiting properties of weights and knots. Some representative examples verify the properties and results. | Curve and surface construction based on the generalized toric-Bernstein
basis functions | 10,243 |
We present a simple and efficient method for refining maps or correspondences by iterative upsampling in the spectral domain that can be implemented in a few lines of code. Our main observation is that high quality maps can be obtained even if the input correspondences are noisy or are encoded by a small number of coefficients in a spectral basis. We show how this approach can be used in conjunction with existing initialization techniques across a range of application scenarios, including symmetry detection, map refinement across complete shapes, non-rigid partial shape matching and function transfer. In each application we demonstrate an improvement with respect to both the quality of the results and the computational speed compared to the best competing methods, with up to two orders of magnitude speed-up in some applications. We also demonstrate that our method is both robust to noisy input and is scalable with respect to shape complexity. Finally, we present a theoretical justification for our approach, shedding light on structural properties of functional maps. | ZoomOut: Spectral Upsampling for Efficient Shape Correspondence | 10,244 |
We present a technique for rendering highly complex 3D scenes in real-time by generating uniformly distributed points on the scene's visible surfaces. The technique is applicable to a wide range of scene types, like scenes directly based on complex and detailed CAD data consisting of billions of polygons (in contrast to scenes handcrafted solely for visualization). This allows to visualize such scenes smoothly even in VR on a HMD with good image quality, while maintaining the necessary frame-rates. In contrast to other point based rendering methods, we place points in an approximated blue noise distribution only on visible surfaces and store them in a highly GPU efficient data structure, allowing to progressively refine the number of rendered points to maximize the image quality for a given target frame rate. Our evaluation shows that scenes consisting of a high amount of polygons can be rendered with interactive frame rates with good visual quality on standard hardware. | Rendering of Complex Heterogenous Scenes using Progressive Blue Surfels | 10,245 |
While many algorithms exist for tracing various contours for illustrating a meshed object, few algorithms organize these contours into region-bounding closed loops. Tracing closed-loop boundaries on a mesh can be problematic due to switchbacks caused by subtle surface variation, and the organization of these regions into a planar map can lead to many small region components due to imprecision and noise. This paper adapts "snaxels," an energy minimizing active contour method designed for robust mesh processing, and repurposes it to generate visual, shadow and shading contours, and a simplified visual-surface planar map, useful for stylized vector art illustration of the mesh. The snaxel active contours can also track contours as the mesh animates, and frame-to-frame correspondences between snaxels lead to a new method to convert the moving contours on a 3-D animated mesh into 2-D SVG curve animations for efficient embedding in Flash, PowerPoint and other dynamic vector art platforms. | Snaxels on a Plane | 10,246 |
Users frequently seek to fabricate objects whose outer surfaces consist of regions with different surface attributes, such as color or material. Manufacturing such objects in a single piece is often challenging or even impossible. The alternative is to partition them into single-attribute volumetric parts that can be fabricated separately and then assembled to form the target object. Facilitating this approach requires partitioning the input model into parts that conform to the surface segmentation and that can be moved apart with no collisions. We propose Surface2Volume, a partition algorithm capable of producing such assemblable parts, each of which is affiliated with a single attribute, the outer surface of whose assembly conforms to the input surface geometry and segmentation. In computing the partition we strictly enforce conformity with surface segmentation and assemblability, and optimize for ease of fabrication by minimizing part count, promoting part simplicity, and simplifying assembly sequencing. We note that computing the desired partition requires solving for three types of variables: per-part assembly trajectories, partition topology, i.e. the connectivity of the interface surfaces separating the different parts, and the geometry, or location, of these interfaces. We efficiently produce the desired partitions by addressing one type of variables at a time: first computing the assembly trajectories, then determining interface topology, and finally computing interface locations that allow parts assemblability. We algorithmically identify inputs that necessitate sequential assembly, and partition these inputs gradually by computing and disassembling a subset of assemblable parts at a time. We demonstrate our method.... | Surface2Volume: Surface Segmentation Conforming Assemblable Volumetric
Partition | 10,247 |
Micro-appearance models have brought unprecedented fidelity and details to cloth rendering. Yet, these models neglect fabric mechanics: when a piece of cloth interacts with the environment, its yarn and fiber arrangement usually changes in response to external contact and tension forces. Since subtle changes of a fabric's microstructures can greatly affect its macroscopic appearance, mechanics-driven appearance variation of fabrics has been a phenomenon that remains to be captured. We introduce a mechanics-aware model that adapts the microstructures of cloth yarns in a physics-based manner. Our technique works on two distinct physical scales: using physics-based simulations of individual yarns, we capture the rearrangement of yarn-level structures in response to external forces. These yarn structures are further enriched to obtain appearance-driving fiber-level details. The cross-scale enrichment is made practical through a new parameter fitting algorithm for simulation, an augmented procedural yarn model coupled with a custom-design regression neural network. We train the network using a dataset generated by joint simulations at both the yarn and the fiber levels. Through several examples, we demonstrate that our model is capable of synthesizing photorealistic cloth appearance in a %dynamic and mechanically plausible way. | Mechanics-Aware Modeling of Cloth Appearance | 10,248 |
Popular Virtual Reality (VR) tools allow users to draw varying-width, ribbon-like 3D brush strokes by moving a hand-held controller in 3D space. Artists frequently use dense collections of such strokes to draw virtual 3D shapes. We propose SurfaceBrush, a surfacing method that converts such VR drawings into user-intended manifold free-form 3D surfaces, providing a novel approach for modeling 3D shapes. The inputs to our method consist of dense collections of artist-drawn stroke ribbons described by the positions and normals of their central polylines, and ribbon widths. These inputs are highly distinct from those handled by existing surfacing frameworks and exhibit different sparsity and error patterns, necessitating a novel surfacing approach. We surface the input stroke drawings by identifying and leveraging local coherence between nearby artist strokes. In particular, we observe that strokes intended to be adjacent on the artist imagined surface often have similar tangent directions along their respective polylines. We leverage this local stroke direction consistency by casting the computation of the user-intended manifold surface as a constrained matching problem on stroke polyline vertices and edges. We first detect and smoothly connect adjacent similarly-directed sequences of stroke edges producing one or more manifold partial surfaces. We then complete the surfacing process by identifying and connecting adjacent similarly directed edges along the borders of these partial surfaces. We confirm the usability of the SurfaceBrush interface and the validity of our drawing analysis via an observational study. We validate our stroke surfacing algorithm by demonstrating an array of manifold surfaces computed by our framework starting from a range of inputs of varying complexity, and by comparing our outputs to reconstructions computed using alternative means. | SurfaceBrush: From Virtual Reality Drawings to Manifold Surfaces | 10,249 |
Little is known about how people learn from a brief glimpse of three-dimensional (3D) bivariate vector field visualizations and about how well visual features can guide behavior. Here we report empirical study results on the use of color, texture, and length to guide viewing of bivariate glyphs: these three visual features are mapped to the first integer variable (v1) and length to the second quantitative variable (v2). Participants performed two tasks within 20 seconds: (1) MAX: find the largest v2 when v1 is fixed; (2) SEARCH: find a specific bivariate variable shown on the screen in a vector field. Our first study with eighteen participants performing these tasks showed that the randomized vector positions, although they lessened viewers' ability to group vectors, did not reduce task accuracy compared to structured vector fields. This result may support that these color, texture, and length can provide to a certain degree, guide viewers' attention to task-relevant regions. The second study measured eye movement to quantify viewers' behaviors with three-errors (scanning, recognition, and decision errors) and one-behavior (refixation) metrics. Our results showed two dominant search strategies: drilling and scanning. Coloring tended to restrict eye movement to the task-relevant regions of interest, enabling drilling. Length tended to support scanners who quickly wandered around at different v1 levels. Drillers had significantly less errors than scanners and the error rates for color and texture were also lowest. And length had limited discrimination power than color and texture as a 3D visual guidance. Our experiment results may suggest that using categorical visual feature could help obtain the global structure of a vector field visualization. We provide the first benchmark of the attention cost of seeing a bivariate vector on average about 5 items per second. | What Do People See in a Twenty-Second Glimpse of Bivariate Vector Field
Visualizations? | 10,250 |
We present study results from two experiments to empirically validate that separable bivariate pairs for univariate representations of large-magnitude-range vectors are more efficient than integral pairs. The first experiment with 20 participants compared: one integral pair, three separable pairs, and one redundant pair, which is a mix of the integral and separable features. Participants performed three local tasks requiring reading numerical values, estimating ratio, and comparing two points. The second 18-participant study compared three separable pairs using three global tasks when participants must look at the entire field to get an answer: find a specific target in 20 seconds, find the maximum magnitude in 20 seconds, and estimate the total number of vector exponents within 2 seconds. Our results also reveal the following: separable pairs led to the most accurate answers and the shortest task execution time, while integral dimensions were among the least accurate; it achieved high performance only when a pop-out separable feature (here color) was added. To reconcile this finding with the existing literature, our second experiment suggests that the higher the separability, the higher the accuracy; the reason is probably that the emergent global scene created by the separable pairs reduces the subsequent search space. | Picturing Bivariate Separable-Features for Univariate Vector Magnitudes
in Large-Magnitude-Range Quantum Physics Data | 10,251 |
We introduce a novel approach to measure the behavior of a geometric operator before and after coarsening. By comparing eigenvectors of the input operator and its coarsened counterpart, we can quantitatively and visually analyze how well the spectral properties of the operator are maintained. Using this measure, we show that standard mesh simplification and algebraic coarsening techniques fail to maintain spectral properties. In response, we introduce a novel approach for spectral coarsening. We show that it is possible to significantly reduce the sampling density of an operator derived from a 3D shape without affecting the low-frequency eigenvectors. By marrying techniques developed within the algebraic multigrid and the functional maps literatures, we successfully coarsen a variety of isotropic and anisotropic operators while maintaining sparsity and positive semi-definiteness. We demonstrate the utility of this approach for applications including operator-sensitive sampling, shape matching, and graph pooling for convolutional neural networks. | Spectral Coarsening of Geometric Operators | 10,252 |
The root is an important organ of a plant since it is responsible for water and nutrient uptake. Analyzing and modelling variabilities in the geometry and topology of roots can help in assessing the plant's health, understanding its growth patterns, and modeling relations between plant species and between plants and their environment. In this article, we develop a framework for the statistical analysis and modeling of the geometry and topology of plant roots. We represent root structures as points in a tree-shape space equipped with a metric that quantifies geometric and topological differences between pairs of roots. We then use these building blocks to compute geodesics, i.e., optimal deformations under the metric between root structures, and to perform statistical analysis on root populations. We demonstrate the utility of the proposed framework through an application to a dataset of wheat roots grown in different environmental conditions. We also show that the framework can be used in various applications including classification and regression. | Statistical Analysis and Modeling of the Geometry and Topology of Plant
Roots | 10,253 |
Computing a quasi-developable strip surface bounded by design curves finds wide industrial applications. Existing methods compute discrete surfaces composed of developable lines connecting sampling points on input curves which are not adequate for generating smooth quasi-developable surfaces. We propose the first method which is capable of exploring the full solution space of continuous input curves to compute a smooth quasi-developable ruled surface with as large developability as possible. The resulting surface is exactly bounded by the input smooth curves and is guaranteed to have no self-intersections. The main contribution is a variational approach to compute a continuous mapping of parameters of input curves by minimizing a function evaluating surface developability. Moreover, we also present an algorithm to represent a resulting surface as a B-spline surface when input curves are B-spline curves. | Smooth quasi-developable surfaces bounded by smooth curves | 10,254 |
Current quadratic smoothness energies for curved surfaces either exhibit distortions near the boundary due to zero Neumann boundary conditions, or they do not correctly account for intrinsic curvature, which leads to unnatural-looking behavior away from the boundary. This leads to an unfortunate trade-off: one can either have natural behavior in the interior, or a distortion-free result at the boundary, but not both. We introduce a generalized Hessian energy for curved surfaces, expressed in terms of the covariant one-form Dirichlet energy, the Gaussian curvature, and the exterior derivative. Energy minimizers solve the Laplace-Beltrami biharmonic equation, correctly accounting for intrinsic curvature, leading to natural-looking isolines. On the boundary, minimizers are as-linear-as-possible, which reduces the distortion of isolines at the boundary. We discretize the covariant one-form Dirichlet energy using Crouzeix-Raviart finite elements, arriving at a discrete formulation of the Hessian energy for applications on curved surfaces. We observe convergence of the discretization in our experiments. | A Smoothness Energy without Boundary Distortion for Curved Surfaces | 10,255 |
In this paper we propose a fully automatic method for shape correspondence that is widely applicable, and especially effective for non isometric shapes and shapes of different topology. We observe that fully-automatic shape correspondence can be decomposed as a hybrid discrete/continuous optimization problem, and we find the best sparse landmark correspondence, whose sparse-to-dense extension minimizes a local metric distortion. To tackle the combinatorial task of landmark correspondence we use an evolutionary genetic algorithm, where the local distortion of the sparse-to-dense extension is used as the objective function. We design novel geometrically guided genetic operators, which, when combined with our objective, are highly effective for non isometric shape matching. Our method outperforms state of the art methods for automatic shape correspondence both quantitatively and qualitatively on challenging datasets. | ENIGMA: Evolutionary Non-Isometric Geometry Matching | 10,256 |
Recently, instant level labeling for supervised machine learning requires a considerable number of filled masks. In this paper, we propose an efficient automatic region filling algorithm for complicated regions. Distinguishing between adjacent connected regions, the Main Filling Process scans through all pixels and fills all the pixels except boundary ones with either exterior or interior label color. In this way, we succeed in classifying all the pixels inside the region except boundary ones in the given image to form two groups: a background group and a mask group. We then set all exterior label pixels to background color, and interior label pixels to mask color. With this algorithm, we are able to generate output masks precisely and efficiently even for complicated regions as long as boundary pixels are given. Experimental results show that the proposed algorithm can generate precise masks that allow for various machine learning tasks such as supervised training. This algorithm can effectively handle multiple regions, complicated `holes' and regions whose boundaries touch the image border. By testing the algorithm on both toy and practical images, we show that the performance of Scan-flood Fill(SCAFF) has achieved favorable results. | Scan-flood Fill(SCAFF): an Efficient Automatic Precise Region Filling
Algorithm for Complicated Regions | 10,257 |
Many lighting methods used in computer graphics such as indirect illumination can have very high computational costs and need to be approximated for real-time applications. These costs can be reduced by means of upsampling techniques which tend to introduce artifacts and affect the visual quality of the rendered image. This paper suggests a versatile approach for accelerating the rendering of screen space methods while maintaining the visual quality. This is achieved by exploiting the low frequency nature of many of these illumination methods and the geometrical continuity of the scene. First the screen space is dynamically divided into separate sub-images, then the illumination is rendered for each sub-image in an adequate resolution and finally the sub-images are put together in order to compose the final image. Therefore we identify edges in the scene and generate masks precisely specifying which part of the image is included in which sub-image. The masks therefore determine which part of the image is rendered in which resolution. A step wise upsampling and merging process then allows optically soft transitions between the different resolution levels. For this paper, the introduced multi-resolution rendering method was implemented and tested on three commonly used lighting methods. These are screen space ambient occlusion, soft shadow mapping and screen space global illumination. | Multi-Resolution Rendering for Computationally Expensive Lighting
Effects | 10,258 |
In this paper we demonstrate robust estimation of the model parameters of a fully-linear data-driven BRDF model from a reflectance map under known natural lighting. To regularize the estimation of the model parameters, we leverage the reflectance similarities within a material class. We approximate the space of homogeneous BRDFs using a Gaussian mixture model, and assign a material class to each Gaussian in the mixture model. We formulate the estimation of the model parameters as a non-linear maximum a-posteriori optimization, and introduce a linear approximation that estimates a solution per material class from which the best solution is selected. We demonstrate the efficacy and robustness of our method using the MERL BRDF database under a variety of natural lighting conditions, and we provide a proof-of-concept real-world experiment. | Estimating Homogeneous Data-driven BRDF Parameters from a Reflectance
Map under Known Natural Lighting | 10,259 |
This paper proposes a novel method for modeling human retinal cone distribution. It is based on Blue-noise sampling algorithms that share interesting properties with the sampling performed by the mosaic formed by cone photoreceptors in the retina. Here we present the method together with a series of examples of various real retinal patches. The same samples have also been created with alternative algorithms and compared with plots of the center of the inner segments of cone photoreceptors from imaged retinas. Results are evaluated with different distance measure used in the field, like nearest-neighbor analysis and pair correlation function. The proposed method can describe features of a human retinal cone distribution with a certain degree of similarity to the available data and can be efficiently used for modeling local patches of retina. | Blue-noise sampling for human retinal cone spatial distribution modeling | 10,260 |
We extend the formulation of position-based rods to include elastic volumetric deformations. We achieve this by introducing an additional degree of freedom per vertex -- isotropic scale (and its velocity). Including scale enriches the space of possible deformations, allowing the simulation of volumetric effects, such as a reduction in cross-sectional area when a rod is stretched. We rigorously derive the continuous formulation of its elastic energy potentials, and hence its associated position-based dynamics (PBD) updates to realize this model, enabling the simulation of up to 26000 DOFs at 140 Hz in our GPU implementation. We further show how rods can provide a compact alternative to tetrahedral meshes for the representation of complex muscle deformations, as well as providing a convenient representation for collision detection. This is achieved by modeling a muscle as a bundle of rods, for which we also introduce a technique to automatically convert a muscle surface mesh into a rods-bundle. Finally, we show how rods and/or bundles can be skinned to a surface mesh to drive its deformation, resulting in an alternative to cages for real-time volumetric deformation. | VIPER: Volume Invariant Position-based Elastic Rods | 10,261 |
Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. Visualization is often the first step in making sense of data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualizations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we introduce scenery, a flexible VR/AR visualization framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features and example applications, such as its use in VR for microscopy, in the biomedical image analysis software Fiji, or for visualizing agent-based simulations. | scenery: Flexible Virtual Reality Visualization on the Java VM | 10,262 |
Since ancient times, it has been essential to adopting camouflage on the battlefield, whether it is in the forefront, in-depth or the rear. The traditional evaluation method is made up of people opinion. By watching target or looking at the pictures, and determine the effect of camouflage, so it can be more influenced by man's subjective factors. And now, in order to objectively reflect the camouflage effect, we set up a model through using images similarity to evaluate camouflage effect. Image similarity comparison is divided into two main image feature comparison: image color features and texture features of images. We now using computer design camouflage, camouflage pattern design is divided into two aspects of design color and design plaques. For the design of the color, we based on HSV color model, and as for the design of plague, the key steps are the background color edge extraction, we adopt algorithm based on k-means clustering analysis of the method of background color edge extraction. | Camouflage Design of Analysis Based on HSV Color Statistics and K-means
Clustering | 10,263 |
In recent years, personalized fabrication has received considerable attention because of the widespread use of consumer-level three-dimensional (3D) printers. However, such 3D printers have drawbacks, such as long production time and limited output size, which hinder large-scale rapid-prototyping. In this paper, for the time- and cost-effective fabrication of large-scale objects, we propose a hybrid 3D fabrication method that combines 3D printing and the Zometool construction set, which is a compact, sturdy, and reusable structure for infill fabrication. The proposed method significantly reduces fabrication cost and time by printing only thin 3D outer shells. In addition, we design an optimization framework to generate both a Zometool structure and printed surface partitions by optimizing several criteria, including printability, material cost, and Zometool structure complexity. Moreover, we demonstrate the effectiveness of the proposed method by fabricating various large-scale 3D models. | ZomeFab: Cost-effective Hybrid Fabrication with Zometools | 10,264 |
Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph. Such lightweight capture is in stark contrast with the tens or hundreds of pictures required by traditional optimization-based approaches. However, a single image is often simply not enough to observe the rich appearance of real-world materials. We present a deep-learning method capable of estimating material appearance from a variable number of uncalibrated and unordered pictures captured with a handheld camera and flash. Thanks to an order-independent fusing layer, this architecture extracts the most useful information from each picture, while benefiting from strong priors learned from data. The method can handle both view and light direction variation without calibration. We show how our method improves its prediction with the number of input pictures, and reaches high quality reconstructions with as little as 1 to 10 images -- a sweet spot between existing single-image and complex multi-image approaches. | Flexible SVBRDF Capture with a Multi-Image Deep Network | 10,265 |
We identify two major steps in data analysis, data exploration for understanding and observing patterns/relationships in data; and construction, design and assessment of various models to formalize these relationships. For each step, there exists a large set of tools and software. For the first step, many visualization tools exist, such as, GGobi, Parallax, and Crystal Vision, and most recently tableau and plottly. For the second step, many Scientific Computing Environments (SCEs) exist, such as, Matlab, Mathematica, R and Python. However, there does not exist a tool which allows for seamless two-way interaction between visualization tools and SCEs. We have designed and implemented a data visualization platform (DVP) with an architecture and design that attempts to bridge this gap. DVP connects seamlessly to SCEs to bring the computational capabilities to the visualization methods in a single coherent platform. DVP is designed with two interfaces, the desktop stand alone version and the online interface. To illustrate the power of DVP design, a free demo for the online interface of DVP is available \citep{DVP} and very low-level design details are explained in this article. Since DVP was launched, circa 2012, the present manuscript was not published since today for commercialization and patent considerations. | DVP: Data Visualization Platform | 10,266 |
Choosing the right representation for geometry is crucial for making 3D models compatible with existing applications. Focusing on piecewise-smooth man-made shapes, we propose a new representation that is usable in conventional CAD modeling pipelines and can also be learned by deep neural networks. We demonstrate its benefits by applying it to the task of sketch-based modeling. Given a raster image, our system infers a set of parametric surfaces that realize the input in 3D. To capture piecewise smooth geometry, we learn a special shape representation: a deformable parametric template composed of Coons patches. Naively training such a system, however, is hampered by non-manifold artifacts in the parametric shapes and by a lack of data. To address this, we introduce loss functions that bias the network to output non-self-intersecting shapes and implement them as part of a fully self-supervised system, automatically generating both shape templates and synthetic training data. We develop a testbed for sketch-based modeling, demonstrate shape interpolation, and provide comparison to related work. | Learning Manifold Patch-Based Representations of Man-Made Shapes | 10,267 |
In light transport simulation, challenging situations are caused by the variety of materials and the relative length of path segments. Path Tracing can handle many situations and scales well to parallel hardware. However, it is not able to produce paths which have a smooth surface in connection with a small light source. Here, photon transports perform superior, which can be ineffective if the smooth object is small compared to the scene size. We propose to use the last segment of a Path Tracer path as the first segment of a photon path. As a result, the strengths of next event estimation are inherited by the photon transport and photons are guided toward the regions where they are most useful. To that end, we developed a lock-free sparse octree, which we use for fast and robust density estimates. Summarizing, the new method can outperform state of the art algorithms like Vertex Connection and Merging in certain scenarios. | Next Event Backtracking | 10,268 |
Commonly used linear and nonlinear constitutive material models in deformation simulation contain many simplifications and only cover a tiny part of possible material behavior. In this work we propose a framework for learning customized models of deformable materials from example surface trajectories. The key idea is to iteratively improve a correction to a nominal model of the elastic and damping properties of the object, which allows new forward simulations with the learned correction to more accurately predict the behavior of a given soft object. Space-time optimization is employed to identify gentle control forces with which we extract necessary data for model inference and to finally encapsulate the material correction into a compact parametric form. Furthermore, a patch based position constraint is proposed to tackle the challenge of handling incomplete and noisy observations arising in real-world examples. We demonstrate the effectiveness of our method with a set of synthetic examples, as well with data captured from real world homogeneous elastic objects. | Learning Elastic Constitutive Material and Damping Models | 10,269 |
Skeleton-based and cage-based deformation techniques represent the two most popular approaches to control real-time deformations of digital shapes and are, to a vast extent, complementary to one another. Despite their complementary roles, high-end modeling packages do not allow for seamless integration of such control structures, thus inducing a considerable burden on the user to maintain them synchronized. In this paper, we propose a framework that seamlessly combines rigging skeletons and deformation cages, granting artists with a real-time deformation system that operates using any smooth combination of the two approaches. By coupling the deformation spaces of cages and skeletons, we access a much larger space, containing poses that are impossible to obtain by acting solely on a skeleton or a cage. Our method is oblivious to the specific techniques used to perform skinning and cage-based deformation, securing it compatible with pre-existing tools. We demonstrate the usefulness of our hybrid approach on a variety of examples. | Real-time Deformation with Coupled Cages and Skeletons | 10,270 |
Boundary detection has long been a fundamental tool for image processing and computer vision, supporting the analysis of static and time-varying data. In this work, we built upon the theory of Graph Signal Processing to propose a novel boundary detection filter in the context of graphs, having as main application scenario the visual analysis of spatio-temporal data. More specifically, we propose the equivalent for graphs of the so-called Laplacian of Gaussian edge detection filter, which is widely used in image processing. The proposed filter is able to reveal interesting spatial patterns while still enabling the definition of entropy of time slices. The entropy reveals the degree of randomness of a time slice, helping users to identify expected and unexpected phenomena over time. The effectiveness of our approach appears in applications involving synthetic and real data sets, which show that the proposed methodology is able to uncover interesting spatial and temporal phenomena. The provided examples and case studies make clear the usefulness of our approach as a mechanism to support visual analytic tasks involving spatio-temporal data. | GLoG: Laplacian of Gaussian for Spatial Pattern Detection in
Spatio-Temporal Data | 10,271 |
Terrains are visually important and commonly used in computer graphics. While many algorithms for their generation exist, it is difficult to assess the realism of a generated terrain. This paper presents a first step in the direction of perceptual evaluation of terrain models. We gathered and categorized several classes of real terrains and we generated synthetic terrains by using methods from computer graphics. We then conducted two large studies ranking the terrains perceptually and showing that the synthetic terrains are perceived as lacking realism as compared to the real ones. Then we provide insight into the features that affect the perceived realism by a quantitative evaluation based on localized geomorphology-based landform features (geomorphons) that categorize terrain structures such as valleys, ridges, hollows, etc. We show that the presence or absence of certain features have a significant perceptual effect. We then introduce Perceived Terrain Realism Metrics (PTRM); a perceptual metrics that estimates perceived realism of a terrain represented as a digital elevation map by relating distribution of terrain features with their perceived realism. We validated PTRM on real and synthetic data and compared it to the perceptual studies. To confirm the importance of the presence of these features, we used a generative deep neural network to transfer them between real terrains and synthetic ones and we performed another perceptual experiment that further confirmed their importance for perceived realism. | PTRM: Perceived Terrain Realism Metrics | 10,272 |
Measurement is an integral part of modern science, providing the fundamental means for evaluation, comparison, and prediction. In the context of visualization, several different types of measures have been proposed, ranging from approaches that evaluate particular aspects of individual visualization techniques, their perceptual characteristics, and even economic factors. Furthermore, there are approaches that attempt to provide means for measuring general properties of the visualization process as a whole. Measures can be quantitative or qualitative, and one of the primary goals is to provide objective means for reasoning about visualizations and their effectiveness. As such, they play a central role in the development of scientific theories for visualization. In this chapter, we provide an overview of the current state of the art, survey and classify different types of visualization measures, characterize their strengths and drawbacks, and provide an outline of open challenges for future research. | Measures in Visualization Space | 10,273 |
Visualization of large vector line data is a core task in geographic and cartographic systems. Vector maps are often displayed at different cartographic generalization levels, traditionally by using several discrete levels-of-detail (LODs). This limits the generalization levels to a fixed and predefined set of LODs, and generally does not support smooth LOD transitions. However, fast GPUs and novel line rendering techniques can be exploited to integrate dynamic vector map LOD management into GPU-based algorithms for locally-adaptive line simplification and real-time rendering. We propose a new technique that interactively visualizes large line vector datasets at variable LODs. It is based on the Douglas-Peucker line simplification principle, generating an exhaustive set of line segments whose specific subsets represent the lines at any variable LOD. At run time, an appropriate and view-dependent error metric supports screen-space adaptive LOD levels and the display of the correct subset of line segments accordingly. Our implementation shows that we can simplify and display large line datasets interactively. We can successfully apply line style patterns, dynamic LOD selection lenses, and anti-aliasing techniques to our line rendering. | LOCALIS: Locally-adaptive Line Simplification for GPU-based Geographic
Vector Data Visualization | 10,274 |
Low isometric distortion is often required for mesh parameterizations. A configuration of some vertices, where the distortion is concentrated, provides a way to mitigate isometric distortion, but determining the number and placement of these vertices is non-trivial. We call these vertices distortion points. We present a novel and automatic method to detect distortion points using a voting strategy. Our method integrates two components: candidate generation and candidate voting. Given a closed triangular mesh, we generate candidate distortion points by executing a three-step procedure repeatedly: (1) randomly cut an input to a disk topology; (2) compute a low conformal distortion parameterization; and (3) detect the distortion points. Finally, we count the candidate points and generate the final distortion points by voting. We demonstrate that our algorithm succeeds when employed on various closed meshes with a genus of zero or higher. The distortion points generated by our method are utilized in three applications, including planar parameterization, semi-automatic landmark correspondence, and isotropic remeshing. Compared to other state-of-the-art methods, our method demonstrates stronger practical robustness in distortion point detection. | Voting for Distortion Points in Geometric Processing | 10,275 |
We propose a method to simultaneously compute scalar basis functions with an associated functional map for a given pair of triangle meshes. Unlike previous techniques that put emphasis on smoothness with respect to the Laplace--Beltrami operator and thus favor low-frequency eigenfunctions, we aim for a spectrum that allows for better feature matching. This change of perspective introduces many degrees of freedom into the problem which we exploit to improve the accuracy of our computed correspondences. To effectively search in this high dimensional space of solutions, we incorporate into our minimization state-of-the-art regularizers. We solve the resulting highly non-linear and non-convex problem using an iterative scheme via the Alternating Direction Method of Multipliers. At each step, our optimization involves simple to solve linear or Sylvester-type equations. In practice, our method performs well in terms of convergence, and we additionally show that it is similar to a provably convergent problem. We show the advantages of our approach by extensively testing it on multiple datasets in a few applications including shape matching, consistent quadrangulation and scalar function transfer. | Shape Analysis via Functional Map Construction and Bases Pursuit | 10,276 |
To efficiently simulate very thin, inextensible materials like cloth or paper, it is tempting to replace force-based thin-plate dynamics with hard isometry constraints. Unfortunately, naive formulations of the constraints induce membrane locking---artificial stiffening of bending modes due to the inability of discrete kinematics to reproduce exact isometries. We propose a simple set of meshless isometry constraints, based on moving-least-squares averaging of the strain tensor, which do not lock, and which can be easily incorporated into standard constrained Lagrangian dynamics integration. | Locking-free Simulation of Isometric Thin Plates | 10,277 |
Scientific visualization tools tend to be flexible in some ways (e.g., for exploring isovalues) while restricted in other ways, such as working only on regular grids, or only on unstructured meshes (as used in the finite element method, FEM). Our work seeks to expose the common structure of visualization methods, apart from the specifics of how the fields being visualized are formed. Recognizing that previous approaches to FEM visualization depend on efficiently updating computed positions within a mesh, we took an existing visualization domain-specific language, and added a mesh position type and associated arithmetic operators. These are orthogonal to the visualization method itself, so existing programs for visualizing regular grid data work, with minimal changes, on higher-order FEM data. We reproduce the efficiency gains of an earlier guided search method of mesh position update for computing streamlines, and we demonstrate a novel ability to uniformly sample ridge surfaces of higher-order FEM solutions defined on curved meshes. | Point Movement in a DSL for Higher-Order FEM Visualization | 10,278 |
In the context of additive manufacturing we present a novel technique for direct slicing of a dilated or eroded volume, where the input volume boundary is a triangle mesh. Rather than computing a 3D model of the boundary of the dilated or eroded volume, our technique directly produces its slices. This leads to a computationally and memory efficient algorithm, which is embarrassingly parallel. Contours can be extracted under an arbitrary chord error, non-uniform dilation or erosion are also possible. Finally, the scheme is simple and robust to implement. | Efficient Direct Slicing Of Dilated And Eroded 3d Models For Additive
Manufacturing: Technical Report | 10,279 |
A considerable limitation of employing sparse voxels octrees (SVOs) as a model format for ray tracing has been that the octree data structure is inherently static. Due to traversal algorithms' dependence on the strict hierarchical structure of octrees, it has been challenging to achieve real-time performance of SVO model animation in ray tracing since the octree data structure would typically have to be regenerated every frame. Presented in this article is a novel method for animation of models specified on the SVO format. The method distinguishes itself by permitting model transformations such as rotation, translation, and anisotropic scaling, while preserving the hierarchical structure of SVO models so that they may be efficiently traversed. Due to its modest memory footprint and straightforward arithmetic operations, the method is well-suited for implementation in hardware. A software ray tracing implementation of animated SVO models demonstrates real-time performance on current-generation desktop GPUs, and shows that the animation method does not substantially slow down the rendering procedure compared to rendering static SVOs. | Efficient Animation of Sparse Voxel Octrees for Real-Time Ray Tracing | 10,280 |
Special curves in the Minkowski space such as Minkowski Pythagorean hodographs play an important role in Computer Aided Geometric Design, and their usages have been thoroughly studied in the recent years. Also, several papers have been published which describe methods for interpolating Hermite data in R2,1 by MPH curves. Bizzarri et al.introduced the class of RE curves and presented an interpolation method for G1 Hermite data, where the resulting RE curve yields a rational boundary for the represented domain. We now propose a new application area for RE curves: skinning of a discrete set of input circles. We find the appropriate Hermite data to interpolate so that the obtained rational envelope curves touch each circle at previously defined points of contact. This way we overcome the problematic scenarios when the location of the touching points would not be appropriate for skinning purposes. | Applying Rational Envelope curves for skinning purposes | 10,281 |
We propose Hierarchical Optimization Time Integration (HOT) for efficient implicit time-stepping of the Material Point Method (MPM) irrespective of simulated materials and conditions. HOT is an MPM-specialized hierarchical optimization algorithm that solves nonlinear time step problems for large-scale MPM systems near the CFL-limit. HOT provides convergent simulations "out-of-the-box" across widely varying materials and computational resolutions without parameter tuning. As an implicit MPM time stepper accelerated by a custom-designed Galerkin multigrid wrapped in a quasi-Newton solver, HOT is both highly parallelizable and robustly convergent. As we show in our analysis, HOT maintains consistent and efficient performance even as we grow stiffness, increase deformation, and vary materials over a wide range of finite strain, elastodynamic and plastic examples. Through careful benchmark ablation studies, we compare the effectiveness of HOT against seemingly plausible alternative combinations of MPM with standard multigrid and other Newton-Krylov models. We show how these alternative designs result in severe issues and poor performance. In contrast, HOT outperforms the existing state-of-the-art, heavily optimized implicit MPM codes with an up to 10x performance speedup across a wide range of challenging benchmark test simulations. | Hierarchical Optimization Time Integration for CFL-rate MPM Stepping | 10,282 |
Redirected walking is a Virtual Reality(VR) locomotion technique which enables users to navigate virtual environments (VEs) that are spatially larger than the available physical tracked space. In this work we present a novel technique for redirected walking in VR based on the psychological phenomenon of inattentional blindness. Based on the user's visual fixation points we divide the user's view into zones. Spatially-varying rotations are applied according to the zone's importance and are rendered using foveated rendering. Our technique is real-time and applicable to small and large physical spaces. Furthermore, the proposed technique does not require the use of stimulated saccades but rather takes advantage of naturally occurring saccades and blinks for a complete refresh of the framebuffer. We performed extensive testing and present the analysis of the results of three user studies conducted for the evaluation. | Inattentional Blindness for Redirected Walking Using Dynamic Foveated
Rendering | 10,283 |
We present a pipeline for modeling spatially varying BRDFs (svBRDFs) of planar materials which only requires a mobile phone for data acquisition. With a minimum of two photos under the ambient and point light source, our pipeline produces svBRDF parameters, a normal map and a tangent map for the material sample. The BRDF fitting is achieved via a pixel clustering strategy and an optimization based scheme. Our method is light-weight, easy-to-use and capable of producing high-quality BRDF textures. | A SVBRDF Modeling Pipeline using Pixel Clustering | 10,284 |
A debate in the scientific literature has arisen regarding whether the orb depicted in Salvator Mundi, which has been attributed by some experts to Leonardo da Vinci, was rendered in a optically faithful manner or not. Some hypothesize that it was solid crystal while others hypothesize that it was hollow, with competing explanations for its apparent lack of background distortion and its three white spots. In this paper, we study the optical accuracy of the Salvator Mundi using physically based rendering, a sophisticated computer graphics tool that produces optically accurate images by simulating light transport in virtual scenes. We created a virtual model of the composition centered on the translucent orb in the subject's hand. By synthesizing images under configurations that vary illuminations and orb material properties, we tested whether it is optically possible to produce an image that renders the orb similarly to how it appears in the painting. Our experiments show that an optically accurate rendering qualitatively matching that of the painting is indeed possible using materials, light sources, and scientific knowledge available to Leonardo da Vinci circa 1500. We additionally tested alternative theories regarding the composition of the orb, such as that it was a solid calcite ball, which provide empirical evidence that such alternatives are unlikely to produce images similar to the painting, and that the orb is instead hollow. | On the Optical Accuracy of the Salvator Mundi | 10,285 |
Usual approaches for image recoloring, such as local filtering by transfer functions and global histogram remapping, lack of accurate control or miss small groups of important pixels. In this paper, we introduce a triangle-based structuring of the colors of an image in the RGB space. We present an analysis of image colors in the RGB space showing the theoretical motivation of our triangular abstraction. We illustrate the usefulness of our structure to recolor images. | RGB Point Cloud Manipulation with Triangular Structures for Artistic
Image Recoloring | 10,286 |
In this paper, we use an original ray-tracing domain decomposition method to address image rendering of naturally lighted scenes. This new method allows to particularly analyze rendering problems on parallel architectures, in the case of interactions between light-rays and glass material. Numerical experiments, for medieval glass rendering within the church of the Royaumont abbey, illustrate the performance of the proposed ray-tracing domain decomposition method (DDM) on multi-cores and multi-processors architectures. On one hand, applying domain decomposition techniques increases speedups obtained by parallelizing the computation. On the other hand, for a fixed number of parallel processes, we notice that speedups increase as the number of sub-domains do. | Spectral Domain Decomposition Method for Natural Lighting and Medieval
Glass Rendering | 10,287 |
This paper presents a comprehensive study of interactive rendering techniques for large 3D line sets with transparency. The rendering of transparent lines is widely used for visualizing trajectories of tracer particles in flow fields. Transparency is then used to fade out lines deemed unimportant, based on, for instance, geometric properties or attributes defined along them. Since accurate blending of transparent lines requires rendering the lines in back-to-front or front-to-back order, enforcing this order for 3D line sets with tens or even hundreds of thousands of elements becomes challenging. In this paper, we study CPU and GPU rendering techniques for large transparent 3D line sets. We compare accurate and approximate techniques using optimized implementations and a number of benchmark data sets. We discuss the effects of data size and transparency on quality, performance and memory consumption. Based on our study, we propose two improvements to per-pixel fragment lists and multi-layer alpha blending. The first improves the rendering speed via an improved GPU sorting operation, and the second improves rendering quality via a transparency-based bucketing. | A Comparison of Rendering Techniques for Large 3D Line Sets with
Transparency | 10,288 |
Artistically controlling fluid simulations requires a large amount of manual work by an artist. The recently presented transportbased neural style transfer approach simplifies workflows as it transfers the style of arbitrary input images onto 3D smoke simulations. However, the method only modifies the shape of the fluid but omits color information. In this work, we therefore extend the previous approach to obtain a complete pipeline for transferring shape and color information onto 2D and 3D smoke simulations with neural networks. Our results demonstrate that our method successfully transfers colored style features consistently in space and time to smoke data for different input textures. | Neural Smoke Stylization with Color Transfer | 10,289 |
We propose a method to realistically insert synthetic objects into existing photographs without requiring access to the scene or any additional scene measurements. With a single image and a small amount of annotation, our method creates a physical model of the scene that is suitable for realistically rendering synthetic objects with diffuse, specular, and even glowing materials while accounting for lighting interactions between the objects and the scene. We demonstrate in a user study that synthetic images produced by our method are confusable with real scenes, even for people who believe they are good at telling the difference. Further, our study shows that our method is competitive with other insertion methods while requiring less scene information. We also collected new illumination and reflectance datasets; renderings produced by our system compare well to ground truth. Our system has applications in the movie and gaming industry, as well as home decorating and user content creation, among others. | Rendering Synthetic Objects into Legacy Photographs | 10,290 |
We describe a set of tools for analyzing, visualizing, and assessing architectural/construction progress with unordered photo collections and 3D building models. With our interface, a user guides the registration of the model in one of the images, and our system automatically computes the alignment for the rest of the photos using a novel Structure-from-Motion (SfM) technique; images with nearby viewpoints are also brought into alignment with each other. After aligning the photo(s) and model(s), our system allows a user, such as a project manager or facility owner, to explore the construction site seamlessly in time, monitor the progress of construction, assess errors and deviations, and create photorealistic architectural visualizations. These interactions are facilitated by automatic reasoning performed by our system: static and dynamic occlusions are removed automatically, rendering information is collected, and semantic selection tools help guide user input. We also demonstrate that our user-assisted SfM method outperforms existing techniques on both real-world construction data and established multi-view datasets. | ConstructAide: Analyzing and Visualizing Construction Sites through
Photographs and Building Models | 10,291 |
Most ray tracing libraries allow the user to provide custom functionality that is executed when a potential ray surface interaction was encountered to determine if the interaction was valid or traversal should be continued. This is e.g. useful for alpha mask validation and allows the user to reuse existing ray object intersection routines rather than reimplementing them. Augmenting ray traversal with custom intersection logic requires some kind of callback mechanism that injects user code into existing library routines. With template libraries, this injection can happen statically since the user compiles the binary code herself. We present an implementation of this "custom intersector" approach and its integration into the C++ ray tracing template library Visionaray. | Adding Custom Intersectors to the C++ Ray Tracing Template Library
Visionaray | 10,292 |
Traditional indoor scene synthesis methods often take a two-step approach: object selection and object arrangement. Current state-of-the-art object selection approaches are based on convolutional neural networks (CNNs) and can produce realistic scenes for a single room. However, they cannot be directly extended to synthesize style-compatible scenes for multiple rooms with different functions. To address this issue, we treat the object selection problem as combinatorial optimization based on a Labeled LDA (L-LDA) model. We first calculate occurrence probability distribution of object categories according to a topic model, and then sample objects from each category considering their function diversity along with style compatibility, while regarding not only separate rooms, but also associations among rooms. User study shows that our method outperforms the baselines by incorporating multi-function and multi-room settings with style constraints, and sometimes even produces plausible scenes comparable to those produced by professional designers. | Style-compatible Object Recommendation for Multi-room Indoor Scene
Synthesis | 10,293 |
This paper presents a new curved layer volume decomposition method for multi-axis support-free printing of freeform solid parts. Given a solid model to be printed that is represented as a tetrahedral mesh, we first establish a geodesic distance field embedded on the mesh, whose value at any vertex is the geodesic distance to the base of the model. Next, the model is naturally decomposed into curved layers by interpolating a number of iso-geodesic distance surfaces (IGDSs). These IGDSs morph from bottom-up in an intrinsic and smooth way owing to the nature of geodesics, which will be used as the curved printing layers that are friendly to multi-axis printing. In addition, to cater to the collision-free requirement and to improve the printing efficiency, we also propose a printing sequence optimization algorithm for determining the printing order of the IGDSs, which helps reduce the air-move path length. Ample experiments in both computer simulation and physical printing are performed, and the experimental results confirm the advantages of our method. | Geodesic Distance Field-based Curved Layer Volume Decomposition for
Multi-Axis Support-free Printing | 10,294 |
Edge-preserving image smoothing is a fundamental procedure for many computer vision and graphic applications. There is a tradeoff between the smoothing quality and the processing speed: the high smoothing quality usually requires a high computational cost which leads to the low processing speed. In this paper, we propose a new global optimization based method, named iterative least squares (ILS), for efficient edge-preserving image smoothing. Our approach can produce high-quality results but at a much lower computational cost. Comprehensive experiments demonstrate that the propose method can produce results with little visible artifacts. Moreover, the computation of ILS can be highly parallel, which can be easily accelerated through either multi-thread computing or the GPU hardware. With the acceleration of a GTX 1080 GPU, it is able to process images of 1080p resolution ($1920\times1080$) at the rate of 20fps for color images and 47fps for gray images. In addition, the ILS is flexible and can be modified to handle more applications that require different smoothing properties. Experimental results of several applications show the effectiveness and efficiency of the proposed method. The code is available at \url{https://github.com/wliusjtu/Real-time-Image-Smoothing-via-Iterative-Least-Squares} | Real-time Image Smoothing via Iterative Least Squares | 10,295 |
Minimizing the Gaussian curvature of meshes can play a fundamental role in 3D mesh processing. However, there is a lack of computationally efficient and robust Gaussian curvature optimization method. In this paper, we present a simple yet effective method that can efficiently reduce Gaussian curvature for 3D meshes. We first present the mathematical foundation of our method. Then, we introduce a simple and robust implicit Gaussian curvature optimization method named Gaussian Curvature Filter (GCF). GCF implicitly minimizes Gaussian curvature without the need to explicitly calculate the Gaussian curvature itself. GCF is highly efficient and this method can be used in a large range of applications that involve Gaussian curvature. We conduct extensive experiments to demonstrate that GCF significantly outperforms state-of-the-art methods in minimizing Gaussian curvature, and geometric feature preserving soothing on 3D meshes. GCF program is available at https://github.com/tangwenming/GCF-filter. | Gaussian Curvature Filter on 3D Meshes | 10,296 |
Movie productions use high resolution 3d characters with complex proprietary rigs to create the highest quality images possible for large displays. Unfortunately, these 3d assets are typically not compatible with real-time graphics engines used for games, mixed reality and real-time pre-visualization. Consequently, the 3d characters need to be re-modeled and re-rigged for these new applications, requiring weeks of work and artistic approval. Our solution to this problem is to learn a compact image-based rendering of the original 3d character, conditioned directly on the rig parameters. Our idea is to render the character in many different poses and views, and to train a deep neural network to render high resolution images, from the rig parameters directly. Many neural rendering techniques have been proposed to render from 2d skeletons, or geometry and UV maps. However these require manual work, and to do not remain compatible with the animator workflow of manipulating rig widgets, as well as the real-time game engine pipeline of interpolating rig parameters. We extend our architecture to support dynamic re-lighting and composition with other 3d objects in the scene. We designed a network that efficiently generates multiple scene feature maps such as normals, depth, albedo and mask, which are composed with other scene objects to form the final image. | Rig-space Neural Rendering | 10,297 |
The design of functional seating furniture is a complicated process which often requires extensive manual design effort and empirical evaluation. We propose a computational design framework for pose-driven automated generation of body-supports which are optimized for comfort of sitting. Given a human body in a specified pose as input, our method computes an approximate pressure distribution that also takes frictional forces and body torques into consideration which serves as an objective measure of comfort. Utilizing this information to find out where the body needs to be supported in order to maintain comfort of sitting, our algorithm can create a supporting mesh suited for a person in that specific pose. This is done in an automated fitting process, using a template model capable of supporting a large variety of sitting poses. The results can be used directly or can be considered as a starting point for further interactive design. | Pose to Seat: Automated Design of Body-Supporting Surfaces | 10,298 |
In this paper alternative method for real-time 3D model rasterization is given. Surfaces are drawn in perspective-map space which acts as a virtual camera lens. It can render single-pass 360{\deg} angle of view (AOV) image of unlimited shape, view-directions count and unrestrained projection geometry (e.g. direct lens distortion, projection mapping, curvilinear perspective), natively aliasing-free. In conjunction to perspective vector map, visual-sphere perspective model is proposed. A model capable of combining pictures from sources previously incompatible, like fish-eye camera and wide-angle lens picture. More so, method is proposed for measurement and simulation of a real optical system variable no-parallax point (NPP). This study also explores philosophical and historical aspects of picture perception and presents a guide for perspective design. | Perspective picture from Visual Sphere: a new approach to image
rasterization | 10,299 |
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