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"content": "High-dimensional data is emerging in more and more varied domains, but its analysis has revealed to be difficult due to the curse of dimensionality. Dimension reduction emerged as a powerful tool in overcoming problems related to high-dimensionality, still the curse of dimensionality continues to impact many of the existing methods. The current paper concentrates on low-dimensional distance-based embeddings for high-dimensional multimodal distributions, i.e. clustered data. Pair wise distances are particularly influenced by high-dimensionality. Their analysis is at the basis of the embedding method presented here and called HDME. To avoid the problems of high-dimensionality, HDME performs a distance transformation based on interpoint relationships. The positive influence of the transformation in preserving and emphasizing clusters is first demonstrated using label information. The distance transformation is driven by the estimation of the neighbourhood information. The transformed distances are embedded in a low-dimensional space using a classical embedding method. Experiments on real-world data show that distance transformations can be effectively used in conjunction with distance-based embedding methods to obtain representation spaces that well discriminate clusters.",
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"abstract": "Consider a problem of estimating an unknown high dimensional density whose support lies on unknown low-dimensional data manifold. This problem arises in many data mining tasks, and the paper proposes a new geometrically motivated solution for the problem in manifold learning framework, including an estimation of an unknown support of the density. Firstly, tangent bundle manifold learning problem is solved resulting in transforming high dimensional data into their low-dimensional features and estimating the Riemannian tensor on the Data manifold. After that, an unknown density of the constructed features is estimated with the use of appropriate kernel approach. Finally, with the use of estimated Riemannian tensor, the final estimator of the initial density is constructed.",
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"abstract": "Redirected walking enables natural walking in virtual reality under the constraints of limited physical space by manipulating position and orientation of the user. One such manipulation method are rotation gains which decouple real and virtual head turning. When reaching a boundary of the tracking space, the user's orientation can be reset by applying a rotation gain and forcing the user to do a certain task that requires head rotations. To identify what kind of tasks are best suited to mask the redirection, four tasks were designed that differentiate in their amount of mentally and physically demand: a spatial memory task, a verbal memory task, a physically exhausting task and a task which requires physical skill. A first pilot study was conducted to evaluate the influence that the different task categories have on the redirection awareness. The results showed overall low redirection detection rates, with the physically exhausting task being the least noticeable, whilst the physical skill task was detected the most, and the mentally demanding tasks in between. Although no clear distinction regarding the redirection awareness between mentally and physically demanding tasks could be shown, the insights gained from this work and the study, can be used to improve future implementations of rotation tasks.",
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"abstract": "Recently, Japanese pop culture has attracted attention all over the world. In this research, by focusing on the comic, which is one of the Japanese culture, We have developed a entertainment which user can enjoy easily. Character design of animation and comic has been sensitively reflects the fashion of the era. So, We investigated features of the comics that were popular in each generation from the 1960s to the 2000s in Japan, and analyzed the correlation between them and fashion or design that were popular at that generation. Based on the results of research and analysis, we developed and implemented the entertainment applications that generate an avatar that reflects the age and generation. When users make an Avatar by choosing the fashion and hairstyle of choice, it determines whether the user's sense is closer to which generation.",
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"abstract": "Traditional 3D morphable face models (3DMMs) provide fine-grained control over expression but cannot easily capture geometric and appearance details. Neural volumetric representations approach photorealism but are hard to animate and do not generalize well to unseen expressions. To tackle this problem, we propose IMavatar (Implicit Morphable avatar), a novel method for learning implicit head avatars from monocular videos. Inspired by the fine-grained control mechanisms afforded by conventional 3DMMs, we represent the expression- and pose-related deformations via learned blendshapes and skinning fields. These attributes are pose-independent and can be used to morph the canonical geometry and texture fields given novel expression and pose parameters. We employ ray marching and iterative root-finding to locate the canonical surface intersection for each pixel. A key contribution is our novel analytical gradient formulation that enables end-to-end training of IMavatars from videos. We show quantitatively and qualitatively that our method improves geometry and covers a more complete expression space compared to state-of-the-art methods. Code and data can be found at https://ait.ethz.ch/projects/2022/IMavatar/.",
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"doi": "10.1109/CW55638.2022.00018",
"title": "A Semantics-aware Method for Adding 3D Window Details to Textured LoD2 CityGML Models",
"normalizedTitle": "A Semantics-aware Method for Adding 3D Window Details to Textured LoD2 CityGML Models",
"abstract": "3D window details for buildings are important in many 3D simulation and visualization applications. However, they are not easy to acquire or reconstruct. Thus, many 3D city models have no 3D windows, but only 2D planar textures for their façades (i.e., textured LoD2 CityGML models). Many procedural methods have been proposed to generate 3D façade details from images. However, they usually require tedious efforts to create a procedural grammar to achieve desired results, and lack consideration of window semantics which is a useful building property. In this paper, we propose a novel semantics-aware method for adding 3D window details to textured LoD2 CityGML models. We propose a two-level deep learning-based windowpane detection followed by processing and adjusting the detection results then generating and adding 3D windows to the building models. Different from existing methods, we focus on adding window details considering the semantics (i.e., frames and panes). Moreover, our method does not require tedious reconstruction or grammar creation efforts. It extracts the information present in the texture itself only, finds and adjusts the patterns and shapes from the detection results in an unsupervised and efficient manner to achieve neat window parsing results. Specifically, we propose clustering-based window/pane alignment, neatness-based window image voting, grid-based symmetry and thickness filtering, and fitting-based window-top modeling. Experiments on representative 3D city datasets and illustrative applications demonstrate the effectiveness and usefulness of our method.",
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"content": "3D window details for buildings are important in many 3D simulation and visualization applications. However, they are not easy to acquire or reconstruct. Thus, many 3D city models have no 3D windows, but only 2D planar textures for their façades (i.e., textured LoD2 CityGML models). Many procedural methods have been proposed to generate 3D façade details from images. However, they usually require tedious efforts to create a procedural grammar to achieve desired results, and lack consideration of window semantics which is a useful building property. In this paper, we propose a novel semantics-aware method for adding 3D window details to textured LoD2 CityGML models. We propose a two-level deep learning-based windowpane detection followed by processing and adjusting the detection results then generating and adding 3D windows to the building models. Different from existing methods, we focus on adding window details considering the semantics (i.e., frames and panes). Moreover, our method does not require tedious reconstruction or grammar creation efforts. It extracts the information present in the texture itself only, finds and adjusts the patterns and shapes from the detection results in an unsupervised and efficient manner to achieve neat window parsing results. Specifically, we propose clustering-based window/pane alignment, neatness-based window image voting, grid-based symmetry and thickness filtering, and fitting-based window-top modeling. Experiments on representative 3D city datasets and illustrative applications demonstrate the effectiveness and usefulness of our method.",
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"normalizedAbstract": "3D window details for buildings are important in many 3D simulation and visualization applications. However, they are not easy to acquire or reconstruct. Thus, many 3D city models have no 3D windows, but only 2D planar textures for their façades (i.e., textured LoD2 CityGML models). Many procedural methods have been proposed to generate 3D façade details from images. However, they usually require tedious efforts to create a procedural grammar to achieve desired results, and lack consideration of window semantics which is a useful building property. In this paper, we propose a novel semantics-aware method for adding 3D window details to textured LoD2 CityGML models. We propose a two-level deep learning-based windowpane detection followed by processing and adjusting the detection results then generating and adding 3D windows to the building models. Different from existing methods, we focus on adding window details considering the semantics (i.e., frames and panes). Moreover, our method does not require tedious reconstruction or grammar creation efforts. It extracts the information present in the texture itself only, finds and adjusts the patterns and shapes from the detection results in an unsupervised and efficient manner to achieve neat window parsing results. Specifically, we propose clustering-based window/pane alignment, neatness-based window image voting, grid-based symmetry and thickness filtering, and fitting-based window-top modeling. Experiments on representative 3D city datasets and illustrative applications demonstrate the effectiveness and usefulness of our method.",
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"Image Texture",
"Learning Artificial Intelligence",
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"User Interfaces",
"Window Details",
"Textured Lo D 2 City GML Models",
"Tedious Reconstruction",
"Neat Window Parsing Results",
"Neatness Based Window Image Voting",
"Fitting Based Window Top Modeling",
"Representative 3 D City Datasets",
"Visualization Applications",
"3 D City Models",
"2 D Planar Textures",
"Procedural Methods",
"3 D Fac X 0327 Ade Details",
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"Three Dimensional Displays",
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"Window",
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"Textured Lo D 2 City GML"
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"affiliation": "Fraunhofer Singapore,Singapore",
"fullName": "Xingzi Zhang",
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"surname": "Zhang",
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{
"affiliation": "Singapore Institute of Technology,Singapore",
"fullName": "Kan Chen",
"givenName": "Kan",
"surname": "Chen",
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{
"affiliation": "Fraunhofer Singapore,Singapore",
"fullName": "Henry Johan",
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"surname": "Johan",
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{
"affiliation": "Fraunhofer Singapore,Singapore",
"fullName": "Marius Erdt",
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"title": "2022 12th International Conference on Information Technology in Medicine and Education (ITME)",
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"title": "SFR-Net:A Spatial Feature Enhance Method for Road Extraction",
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"abstract": "In recent years, road remote sensing segmentation has become a necessary prerequisite for map modeling and urban planning. Convolutional neural networks have been widely used in the field of road remote sensing segmentation, and have achieved good performance. However, satellite road photography has the features of distinct objects and huge disparities, making it difficult to extract picture contextual information. We present a SFR-Net based on the mainstream encoder-decoder architecture, as well as an unique multi-scale information fusion enhancement module. The module is inserted between distinct scale features, modeling the interaction of features at different sizes in order to obtain remote sensing picture context information and effectively increase model segmentation precision. The model's performance is validated on CHN6-CUG in this paper, and the precision is enhanced by 1.12% compared to the current mainstream FCN-UNet. As a consequence, we are certain that SFR-Net will become a trustworthy baseline.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In recent years, road remote sensing segmentation has become a necessary prerequisite for map modeling and urban planning. Convolutional neural networks have been widely used in the field of road remote sensing segmentation, and have achieved good performance. However, satellite road photography has the features of distinct objects and huge disparities, making it difficult to extract picture contextual information. We present a SFR-Net based on the mainstream encoder-decoder architecture, as well as an unique multi-scale information fusion enhancement module. The module is inserted between distinct scale features, modeling the interaction of features at different sizes in order to obtain remote sensing picture context information and effectively increase model segmentation precision. The model's performance is validated on CHN6-CUG in this paper, and the precision is enhanced by 1.12% compared to the current mainstream FCN-UNet. As a consequence, we are certain that SFR-Net will become a trustworthy baseline.",
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"normalizedAbstract": "In recent years, road remote sensing segmentation has become a necessary prerequisite for map modeling and urban planning. Convolutional neural networks have been widely used in the field of road remote sensing segmentation, and have achieved good performance. However, satellite road photography has the features of distinct objects and huge disparities, making it difficult to extract picture contextual information. We present a SFR-Net based on the mainstream encoder-decoder architecture, as well as an unique multi-scale information fusion enhancement module. The module is inserted between distinct scale features, modeling the interaction of features at different sizes in order to obtain remote sensing picture context information and effectively increase model segmentation precision. The model's performance is validated on CHN6-CUG in this paper, and the precision is enhanced by 1.12% compared to the current mainstream FCN-UNet. As a consequence, we are certain that SFR-Net will become a trustworthy baseline.",
"fno": "101500a662",
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"givenName": "Changshe",
"surname": "Zhang",
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{
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{
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"title": "2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)",
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"title": "Laplacian ICP for Progressive Registration of 3D Human Head Meshes",
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"abstract": "We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami operator for deformation regularisation, we view the overall process as Laplacian ICP (L-ICP). This exploits a ‘small deformation per iteration’ assumption and is progressively coarse-to-fine, employing an increasingly flexible deformation model, an increasing number of correspondence sets, and increasingly sophisticated correspondence estimation. Correspondence matching is only permitted within predefined vertex subsets derived from domain-specific feature extractors. Additionally, we present a new benchmark and a pair of evaluation metrics for 3D non-rigid registration, based on annotation transfer. We use this to evaluate our framework on a publicly-available dataset of 3D human head scans (Headspace). The method is robust and only requires a small fraction of the computation time compared to the most popular classical approach, yet has comparable registration performance.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami operator for deformation regularisation, we view the overall process as Laplacian ICP (L-ICP). This exploits a ‘small deformation per iteration’ assumption and is progressively coarse-to-fine, employing an increasingly flexible deformation model, an increasing number of correspondence sets, and increasingly sophisticated correspondence estimation. Correspondence matching is only permitted within predefined vertex subsets derived from domain-specific feature extractors. Additionally, we present a new benchmark and a pair of evaluation metrics for 3D non-rigid registration, based on annotation transfer. We use this to evaluate our framework on a publicly-available dataset of 3D human head scans (Headspace). The method is robust and only requires a small fraction of the computation time compared to the most popular classical approach, yet has comparable registration performance.",
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"affiliation": "University of York,Department of Computer Science,UK",
"fullName": "Nick Pears",
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"affiliation": "Mohamed bin Zayed University of Artificial Intelligence (MBZUAI),UAE",
"fullName": "Hang Dai",
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"abstract": "Proper integration of sensory cues facilitates 3D user interaction within virtual environments (VEs). Studies showed that the integration of visual and haptic cues follows maximum likelihood estimation (MLE). Little effort focuses however on the mechanism of integrating force and vibrotactile cues. We thus investigated MLE's suitability for integrating these cues. Within a VE, human users undertook 3D interaction of navigating a flying drone along a high-voltage transmission line for inspection. The users received individual force or vibrotactile cues, and their combinations in collocated and dislocated settings. The users' task performance including completion time and accuracy was assessed under each individual cue and setting. The presence of the vibrotactile cue promoted a better performance than the force cue alone. This agreed with the applicability of tactile cues for sensing 3D surfaces, herein setting a baseline for using MLE. The task performance under the collocated setting indicated a degree of combining the individual cues. In contrast, the performance under the dislocated setting was alike under the individual vibrotactile cue. These observations imply a possible role of MLE in integrating force and vibrotactile cues for 3D user interaction within VEs.",
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"abstract": "Higher order Voronoi diagrams play important roles in settling the problems of k neighbors. Several algorithms for generating higher order Voronoi diagrams have been found until now. However, they all have complex data structures and high price. In this paper, the authors propose a faster algorithm using adaptive partition screen to search for k neighbors locally based on Fixed Upper Bound theorem. The experiment shows that the new algorithm not only can generate many kinds of different higher order Voronoi diagrams, but also has simple data structure and higher speed.",
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"abstract": "The notion of the zone diagram of a finite set of points in the Euclidean plane is an interesting and rich variation of the classical Voronoi diagram, introduced by Asano, Matousek, Tokuyama. Here, we define the more inclusive notion of a maximal zone diagram. The proof of existence of maximal zone diagrams depends on less restrictive initial conditions and is thus conveniently established via Zorn's lemma in contrast to the use of fixed-point theory in proving the existence of a unique zone diagram. A zone diagram is a particular maximal zone diagram satisfying a unique dominance property. We give a characterization for maximal zone diagrams which allows recognition of maximality of certain subsets called subzone diagrams, as well as that of their iterative improvement toward maximality. Maximal zone diagrams offer their own interesting theoretical and computational challenges.",
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"abstract": "Voronoi diagram of points in the Euclidean plane and its computation is foundational to computational geometry. Polynomial root-finding is the origin of fundamental discoveries in all of mathematics and sciences. There is an intrinsic connection between polynomial root-finding in the complex plane and the approximation of Voronoi cells of its roots via a fundamental family of iteration functions, the Basic Family. For instance, the immediate basin of attraction of a root of a complex polynomial under Newton's method is a rough approximation to its Voronoi cell. We formally introduce these connections through the Basic Family of iteration functions, its properties with respect to Voronoi diagrams, and a corresponding visualization called polynomiography. Polynomiography is a medium for art, math, education and science. By making use of the Basic Family we introduce a layering of the points within each Voronoi cell of a polynomial root and study its properties and potential applications. In particular, we prove some novel results about the Basic Family in connection with Voronoi diagrams.",
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"content": "We study Voronoi diagrams for distance functions that add together two convex functions, each taking as its argument the difference between Cartesian coordinates of two planar points. When the functions do not grow too quickly, then the Voronoi diagram has linear complexity and can be constructed in near-linear randomized expected time. Additionally, the level sets of the distances from the sites form a family of pseudocircles in the plane, all cells in the Voronoi diagram are connected, and the set of bisectors separating any one cell in the diagram from each of the others forms an arrangement of pseudolines in the plane. We apply these results to the smoothed distance or biotope transform metric, a geometric analogue of the Jaccard distance whose Voronoi diagrams can be used to determine the dilation of a star network with a given hub. For sufficiently closely spaced points in the plane, the Voronoi diagram of smoothed distance has linear complexity and can be computed efficiently. We also experiment with a variant of Lloyd's algorithm, adapted to smoothed distance, to find uniformly spaced point samples with exponentially decreasing density around a given point.",
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"abstract": "Defocus magnification is the process of rendering a shallow depth-of-field in an image captured using a camera with a narrow aperture. Defocus magnification is a useful tool in photography for emphasis on the subject and for highlighting background bokeh. Estimating the per-pixel blur kernel or the depth-map of the scene followed by spatially-varying re-blurring is the standard approach to defocus magnification. We propose a single-step approach that directly converts a narrow-aperture image to a wide-aperture image. We use a conditional adversarial network trained on multi-aperture images created from light-fields. We use a novel loss term based on a composite focus measure to improve generalization and show high quality defocus magnification.",
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"content": "Defocus magnification is the process of rendering a shallow depth-of-field in an image captured using a camera with a narrow aperture. Defocus magnification is a useful tool in photography for emphasis on the subject and for highlighting background bokeh. Estimating the per-pixel blur kernel or the depth-map of the scene followed by spatially-varying re-blurring is the standard approach to defocus magnification. We propose a single-step approach that directly converts a narrow-aperture image to a wide-aperture image. We use a conditional adversarial network trained on multi-aperture images created from light-fields. We use a novel loss term based on a composite focus measure to improve generalization and show high quality defocus magnification.",
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"abstract": "Fully unsupervised 3D representation learning has gained attention owing to its advantages in data collection. A successful approach involves a viewpoint-aware approach that learns an image distribution based on generative models (e.g., generative adversarial networks (GANs)) while generating various view images based on 3D-aware models (e.g., neural radiance fields (NeRFs)). However, they require images with various views for training, and consequently, their application to datasets with few or limited viewpoints remains a challenge. As a complementary approach, an aperture rendering GAN (AR-GAN) that employs a defocus cue was proposed. However, an AR-GAN is a CNN-based model and represents a defocus independently from a viewpoint change despite its high correlation, which is one of the reasons for its performance. As an alternative to an AR-GAN, we propose an aperture rendering NeRF (AR-NeRF), which can utilize viewpoint and defocus cues in a unified manner by representing both factors in a common ray-tracing framework. Moreover, to learn defocus-aware and defocus-independent representations in a disentangled manner, we propose aperture randomized training, for which we learn to generate images while randomizing the aperture size and latent codes independently. During our experiments, we applied AR-NeRF to various natural image datasets, including flower, bird, and face images, the results of which demonstrate the utility of AR-NeRF for un-supervised learning of the depth and defocus effects.",
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"content": "Fully unsupervised 3D representation learning has gained attention owing to its advantages in data collection. A successful approach involves a viewpoint-aware approach that learns an image distribution based on generative models (e.g., generative adversarial networks (GANs)) while generating various view images based on 3D-aware models (e.g., neural radiance fields (NeRFs)). However, they require images with various views for training, and consequently, their application to datasets with few or limited viewpoints remains a challenge. As a complementary approach, an aperture rendering GAN (AR-GAN) that employs a defocus cue was proposed. However, an AR-GAN is a CNN-based model and represents a defocus independently from a viewpoint change despite its high correlation, which is one of the reasons for its performance. As an alternative to an AR-GAN, we propose an aperture rendering NeRF (AR-NeRF), which can utilize viewpoint and defocus cues in a unified manner by representing both factors in a common ray-tracing framework. Moreover, to learn defocus-aware and defocus-independent representations in a disentangled manner, we propose aperture randomized training, for which we learn to generate images while randomizing the aperture size and latent codes independently. During our experiments, we applied AR-NeRF to various natural image datasets, including flower, bird, and face images, the results of which demonstrate the utility of AR-NeRF for un-supervised learning of the depth and defocus effects.",
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"abstract": "Bokeh effect refers to the soft defocus blur of the background, which can be achieved with different aperture and shutter settings in a camera. In this work, we present a learning-based method for rendering such synthetic depth-of-field effect on input bokeh-free images acquired using ordinary monocular cameras. The proposed network is composed of an efficient densely connected encoder-decoder backbone structure with a pyramid pooling module. Our network leverages the task-specific efficacy of joint intensity estimation and dynamic filter synthesis for the spatially-aware blurring process. Since the rendering task requires distinguishing between large foreground and background regions and their relative depth, our network is further guided by pre-trained salient-region segmentation and depth-estimation modules. Experiments on diverse scenes show that our model elegantly introduces the desired effects in the input images, enhancing their aesthetic quality while maintaining a natural appearance. Along with extensive ablation analysis and visualizations to validate its components, the effectiveness of the proposed network is also demonstrated by achieving the second-highest score in the AIM 2019 Bokeh Effect challenge: fidelity track.",
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"abstract": "Bokeh is an important artistic effect used to highlight the main object of interest on the photo by blurring all out-of-focus areas. While DSLR and system camera lenses can render this effect naturally, mobile cameras are unable to produce shallow depth-of-field photos due to a very small aperture diameter of their optics. Unlike the current solutions simulating bokeh by applying Gaussian blur to image background, in this paper we propose to learn a realistic shallow focus technique directly from the photos produced by DSLR cameras. For this, we present a large-scale bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR with 50mm f/1.8 lenses. We use these images to train a deep learning model to reproduce a natural bokeh effect based on a single narrow-aperture image. The experimental results show that the proposed approach is able to render a plausible non-uniform bokeh even in case of complex input data with multiple objects. The dataset, pre-trained models and codes used in this paper are available on the project website: https://people.ee.ethz.ch/ ihnatova/pynet-bokeh.html.",
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"abstract": "We develop a framework for 3-D shape and motion recovery of articulated deformable objects. We propose a formalism that incorporates the use of implicit surfaces into earlier robotics approaches that were designed to handle articulated structures. We demonstrate its effectiveness for human body modeling from video sequences. Our method is both robust and generic. It could easily be applied to other shape and motion recovery problems.",
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"abstract": "By analyzing the generation of fragmentized reading environment in the age of multimedia information and the changes in reading psychology and reading purpose of the cognitive behaviors under such environment, it is discussed in this paper that under the fragmentized reading environment, the reading psychology of users changes from \"linearity\" to \"nonlinearity\", and the reading content and purpose also present the fragmentized interest points, matched knowledge points and scattered search points from the original \"systematic knowledge\". Therefore, it is concluded in this paper that the design of multimedia reading interface shall have a design concept adaptive to such changes. By discussing the guiding significance to the concept of visual thinking and the interface design, the design of visual hierarchical relationship in the visual thinking and its application in the interactive design of interface design, the practicality and usability values are better used under the current fragmented reading environment, and thus the use efficiency is enhanced.",
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"abstract": "The research presented in this contribution aims to investigate user preferences about how to convey information in Industrial Augmented Reality (IAR) interfaces to the user. Our interest is focused on the opinion of potential technical writers of IAR documentation for assembly or maintenance operations. Authoring of IAR interfaces imply a choice among various visual assets, that is influenced by the information type and the AR display used. There are no specific standards in the literature to follow and it is challenging to extract guidelines from the literature. This study gathers preferences of 105 selected users that have knowledge about IAR issues, graphical user interfaces (GUI) designing, and assembly/maintenance procedures. The results of this survey show a great preference for 3D CAD models of components (product model) for almost all the information types. However, some alternative visual assets have also been proposed, such as video and auxiliary models. Contrary to common practices in industry, text was the least preferred visual asset. The insights from this research can help other IAR technical writers in the authoring of their interfaces.",
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"abstract": "A Cylindrical Elastic Elements (CEE) is proposed to vary joint stiffness of a tendon-driven manipulator. The CEE is made of a cylindrical flexible object or rolling of a blade spring. By inserting the CEEs into routes of wires, the joint stiffness can be changed depending on a set of internal forces. However, it is difficult to obtain the solution of the inverse-kinematics for the manipulators with CEEs because of the complexity of CEE's deformation. This paper presents a numerical solving method for the inverse-kinematics of the one-link manipulator equipped with two CEEs. Using an approximate model of a CEE, we demonstrate the numerical solving method to obtain the wire length from the joint angle and stiffness. The precision of the proposed methods is evaluated by comparison between experimental results and numerical results.",
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"abstract": "The authors extend the integral manifold approach for the control of flexible joint robot manipulators from the known parameter case to the adaptive case. The resulting adaptive law, referred to as the corrective control law, consists of a fast component to damp the fast dynamics and a slow component which is designed based on the integral manifold theory and which consists of a rigid based component along with additional corrective terms. The authors give a detailed derivation of the corrective control law and present tracking results. They illustrate the implementation of the control law using simulation examples and study tracking performance and robustness with respect to an allowable range of stiffness and high adaptation gains.<>",
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"content": "The authors extend the integral manifold approach for the control of flexible joint robot manipulators from the known parameter case to the adaptive case. The resulting adaptive law, referred to as the corrective control law, consists of a fast component to damp the fast dynamics and a slow component which is designed based on the integral manifold theory and which consists of a rigid based component along with additional corrective terms. The authors give a detailed derivation of the corrective control law and present tracking results. They illustrate the implementation of the control law using simulation examples and study tracking performance and robustness with respect to an allowable range of stiffness and high adaptation gains.<>",
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"abstract": "The relationship between tracking accuracy and the model used in dynamics-based control of a vertically articulated manipulator has been experimentally evaluated. High gear ratios reduce the payload sensitivity and computational complexity of the system dynamics but do not eliminate the performance enhancement from accurate compensation of dominant dynamics. Robot controllers must compensate for changes in dominant drive system, link, and payload dynamics to achieve high speed trajectory tracking accuracy. Feedforward dynamic compensation reduced the peak trajectory tracking errors of an independent-joint PD (proportional differential) controller by a factor of three. Knowledge of payload mass and centroid allowed feedforward dynamic compensation to overcome the disturbances produced by unmodeled payload dynamics. The combination of high sample rates, constant PD gains, and adaptive feedforward dynamic compensation produces a control algorithm with excellent payload independent, peak, and final error.<>",
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"abstract": "A novel approach for the control of task-space stiffness characteristics in systems consisting of a superabundance of kinematically dependent inputs is proposed. When there are more input actuations than operational degrees of freedom, internal preloads can be generated that produce effective restoring forces in the face of displacement or disturbances imposed on the system. Examples of this excessive actuation can be found in certain modes of structurally overconstrained parallel manipulators and in antagonistically structured serial manipulators. The input loads are synthesized offline (prior to operation) and entered as a feedforward component so that the desired effective loads on objects are obtained, and (simultaneously) significant disturbances at the task level can be largely rejected in an open-loop fashion. This reduces the burden and shortcomings of standard feedback schemes. Moreover, a layered feedback scheme is used to compensate for small perturbations and unmodeled dynamics. The open-loop task-based stiffness control scheme as applied to structurally parallel mechanism/robotic linkage systems is investigated. The scheme's applicability as a programmable active compliance device is also discussed.<>",
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"abstract": "The point containment predicate which specifies if a point is part of a mathematically well-defined object is a crucial problem in computer graphics. Point-driven algorithms can be executed for several points in parallel as there is no interdependence among the computations for different points. The paper presents a variety of parallel configurations to counteract the main disadvantage of the point-driven algorithm: its slowness on a standard uniprocessor software implementation for interactive editing and manipulation.",
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"abstract": "Let G be an undirected c-edge connected graph. In this paper we give an O(n/sup 2/)-sized planar geometric representation for all edge cuts with capacity less than 6/5c. The representation can be very efficiently built, by using a single run of the Karger-Stein algorithm for finding near-mincuts. We demonstrate that the representation provides an efficient query structure for near-mincuts, as well as a new proof technique through geometric arguments. We show that in algorithms based on edge splitting, computing our representation O(log n) times substitute for one, or sometimes even /spl Omega/(n), u-/spl nu/ mincut computations; this can lead to significant savings, since our representation can be computed /spl theta//spl tilde/(m/n) times faster than the currently best known u-/spl nu/ mincut algorithm. We also improve the running time of the edge augmentation problem, provided the initial edge weights are polynomially bounded.",
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"abstract": "The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to diverse and uncontrollable shape representation cross datasets. To improve discrimination on unseen distribution of point-based geometries in a practical and feasible perspective, this paper proposes a new method of geometry-aware self-training (GAST) for unsupervised domain adaptation of object point cloud classification. Specifically, this paper aims to learn a domain-shared representation of semantic categories, via two novel self-supervised geometric learning tasks as feature regularization. On one hand, the representation learning is empowered by a linear mixup of point cloud samples with their self-generated rotation labels, to capture a global topological configuration of local geometries. On the other hand, a diverse point distribution across datasets can be normalized with a novel curvature-aware distortion localization. Experiments on the PointDA-10 dataset show that our GAST method can significantly outperform the state-of-the-art methods. Source codes and pre-trained models are available at https://github.com/zou-longkun/GAST.",
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