Now showing items 11-18 of 18

    • Modeling and Exploring Co-variations in the Geometry and Configuration of Man-made 3D Shape Families 

      Laga, Hamid; Tabia, Hedi (The Eurographics Association and John Wiley & Sons Ltd., 2017)
      We introduce co-variation analysis as a tool for modeling the way part geometries and configurations co-vary across a family of man-made 3D shapes. While man-made 3D objects exhibit large geometric and structural variations, ...
    • A Parallel Approach to Compression and Decompression of Triangle Meshes using the GPU 

      Jakob, Johannes; Buchenau, Christoph; Guthe, Michael (The Eurographics Association and John Wiley & Sons Ltd., 2017)
      Most state-of-the-art compression algorithms use complex connectivity traversal and prediction schemes, which are not efficient enough for online compression of large meshes. In this paper we propose a scalable massively ...
    • Restricting Voronoi Diagrams to Meshes Using Corner Validation 

      Sainlot, Maxime; Nivoliers, Vincent; Attali, Dominique (The Eurographics Association and John Wiley & Sons Ltd., 2017)
      Restricted Voronoi diagrams are a fundamental geometric structure used in many applications such as surface reconstruction from point sets or optimal transport. Given a set of sites V and a mesh X with vertices in Rd ...
    • The Shape Variational Autoencoder: A Deep Generative Model of Part-segmented 3D Objects 

      Nash, Charlie; Williams, Chris K. I. (The Eurographics Association and John Wiley & Sons Ltd., 2017)
      We introduce a generative model of part-segmented 3D objects: the shape variational auto-encoder (ShapeVAE). The ShapeVAE describes a joint distribution over the existence of object parts, the locations of a dense set of ...
    • Spectral Affine-Kernel Embeddings 

      Budninskiy, Max; Liu, Beibei; Tong, Yiying; Desbrun, Mathieu (The Eurographics Association and John Wiley & Sons Ltd., 2017)
      In this paper, we propose a controllable embedding method for high- and low-dimensional geometry processing through sparse matrix eigenanalysis. Our approach is equally suitable to perform non-linear dimensionality reduction ...
    • Stochastic Heat Kernel Estimation on Sampled Manifolds 

      Aumentado-Armstrong, Tristan; Siddiqi, Kaleem (The Eurographics Association and John Wiley & Sons Ltd., 2017)
      The heat kernel is a fundamental geometric object associated to every Riemannian manifold, used across applications in computer vision, graphics, and machine learning. In this article, we propose a novel computational ...
    • Symposium on Geometry Processing 2017: Frontmatter 

      Bærentzen, Jakob Andreas; Hildebrandt, Klaus (Eurographics Association, 2017)
    • Ternary Sparse Matrix Representation for Volumetric Mesh Subdivision and Processing on GPUs 

      Mueller-Roemer, Johannes Sebastian; Altenhofen, Christian; Stork, André (The Eurographics Association and John Wiley & Sons Ltd., 2017)
      In this paper, we present a novel volumetric mesh representation suited for parallel computing on modern GPU architectures. The data structure is based on a compact, ternary sparse matrix storage of boundary operators. ...