SGP: Eurographics Symposium on Geometry Processing
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SGP18: Eurographics Symposium on Geometry Processing  Posters
ISBN 9783038680697
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Functional Maps on Product Manifolds
(The Eurographics Association, 2018)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 ... 
Solving PDEs on Deconstructed Domains
(The Eurographics Association, 2018)When finding analytical solutions to Partial Differential Equations (PDEs) becomes impossible, it is useful to approximate them via a discrete mesh of the domain. Sometimes a robust triangular (2D) or tetrahedral (3D) mesh ... 
Denoising of Pointclouds Based on Structured Dictionary Learning
(The Eurographics Association, 2018)We formulate the problem of pointcloud denoising in terms of a dictionary learning framework over square surface patches. Assuming that many of the local patches (in the unknown noisefree pointcloud) contain redundancies ... 
Using Mathematical Morphology to Simplify Archaeological Fracture Surfaces
(The Eurographics Association, 2018)It is computationally expensive to fit the highresolution 3D meshes of abraded fragments of archaeological artefacts in a collection. Therefore, simplification of fracture surfaces while preserving the fitting essentials ... 
Outofcore Resampling of Gigantic Point Clouds
(The Eurographics Association, 2018)Nowadays, LiDAR scanners are able to capture complex scenes of real life, leading to extremely detailed point clouds. However, the amount of points acquired (several billions) and their distribution raise the problem of ... 
Frontmatter: Symposium on Geometry Processing 2018  Posters
(The Eurographics Association, 2018) 
Schrödinger Operator for Sparse Approximation of 3D Meshes
(The Eurographics Association, 2017)We introduce a Schrödinger operator for spectral approximation of meshes representing surfaces in 3D. The operator is obtained by modifying the Laplacian with a potential function which defines the rate of oscillation of ... 
PCR: A Geometric Cocktail for Triangulating Point Clouds Beautifully Without Angle Bounds
(The Eurographics Association, 2017)Reconstructing a triangulated surface from a point cloud through a mesh growing algorithm is a difficult problem, in largely because they use bounds for the admissible dihedral angle to decide on the next triangle to be ... 
Localized Manifold Harmonics for Spectral Shape Analysis
(The Eurographics Association, 2017)The use of Laplacian eigenfunctions is ubiquitous in a wide range of computer graphics and geometry processing applications. In particular, Laplacian eigenbases allow generalizing the classical Fourier analysis to manifolds. ... 
A PrimaltoPrimal Discretization of Exterior Calculus on Polygonal Meshes
(The Eurographics Association, 2017)Discrete exterior calculus (DEC) offers a coordinatefree discretization of exterior calculus especially suited for computations on curved spaces. We present an extended version of DEC on surface meshes formed by general ... 
DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels
(The Eurographics Association, 2017)In the context of scene understanding, a variety of methods exists to estimate different information channels from mono or stereo images, including disparity, depth, and normals. Although several advances have been reported ... 
SequentiallyDefined Compressed Modes via ADMM
(The Eurographics Association, 2017)The eigenfunctions of the discrete LaplaceBeltrami operator have played an important role in many aspects of geometry processing. Given the success of sparse representation methods in areas such as compressive sensing it ... 
Symposium on Geometry Processing 2017: Frontmatter
(Eurographics Association, 2017) 
Mesh Statistics for Robust Curvature Estimation
(The Eurographics Association and John Wiley & Sons Ltd., 2016)While it is usually not difficult to compute principal curvatures of a smooth surface of sufficient differentiability, it is a rather difficult task when only a polygonal approximation of the surface is available, because ... 
Deep Learning for Robust Normal Estimation in Unstructured Point Clouds
(The Eurographics Association and John Wiley & Sons Ltd., 2016)Normal estimation in point clouds is a crucial first step for numerous algorithms, from surface reconstruction and scene understanding to rendering. A recurrent issue when estimating normals is to make appropriate decisions ... 
Disk Density Tuning of a Maximal Random Packing
(The Eurographics Association and John Wiley & Sons Ltd., 2016)We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. Starting from any maximal random packing such as a Maximal ... 
Exploration of Empty Space among Spherical Obstacles via Additively Weighted Voronoi Diagram
(The Eurographics Association and John Wiley & Sons Ltd., 2016)Properties of granular materials or molecular structures are often studied on a simple geometric model  a set of 3D balls. If the balls simultaneously change in size by a constant speed, topological properties of the empty ... 
Planar Minimization Diagrams via Subdivision with Applications to Anisotropic Voronoi Diagrams
(The Eurographics Association and John Wiley & Sons Ltd., 2016)Let X = {f1, . . ., fn} be a set of scalar functions of the form fi : R2 →R which satisfy some natural properties. We describe a subdivision algorithm for computing a clustered eisotopic approximation of the minimization ... 
Symmetry and Orbit Detection via LieAlgebra Voting
(The Eurographics Association and John Wiley & Sons Ltd., 2016)In this paper, we formulate an automatic approach to the detection of partial, local, and global symmetries and orbits in arbitrary 3D datasets. We improve upon existing votingbased symmetry detection techniques by ... 
Identifying Style of 3D Shapes using Deep Metric Learning
(The Eurographics Association and John Wiley & Sons Ltd., 2016)We present a method that expands on previous work in learning human perceived style similarity across objects with different structures and functionalities. Unlike previous approaches that tackle this problem with the help ...