GAMesh: Guided and Augmented Meshing for Deep Point Networks

Autor: Nitin Agarwal, Meenakshisundaram Gopi
Rok vydání: 2020
Předmět:
Surface (mathematics)
Computational Geometry (cs.CG)
FOS: Computer and information sciences
Vertex (computer graphics)
Computer Science - Machine Learning
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Point cloud
Computer Science - Computer Vision and Pattern Recognition
Topology (electrical circuits)
02 engineering and technology
Iterative reconstruction
010501 environmental sciences
Network topology
01 natural sciences
Machine Learning (cs.LG)
Computer Science - Graphics
0202 electrical engineering
electronic engineering
information engineering
Point (geometry)
Polygon mesh
0105 earth and related environmental sciences
ComputingMethodologies_COMPUTERGRAPHICS
business.industry
020207 software engineering
Graphics (cs.GR)
Computer Science - Computational Geometry
Artificial intelligence
business
Algorithm
Zdroj: 3DV
DOI: 10.48550/arxiv.2010.09774
Popis: We present a new meshing algorithm called guided and augmented meshing, GAMesh, which uses a mesh prior to generate a surface for the output points of a point network. By projecting the output points onto this prior and simplifying the resulting mesh, GAMesh ensures a surface with the same topology as the mesh prior but whose geometric fidelity is controlled by the point network. This makes GAMesh independent of both the density and distribution of the output points, a common artifact in traditional surface reconstruction algorithms. We show that such a separation of geometry from topology can have several advantages especially in single-view shape prediction, fair evaluation of point networks and reconstructing surfaces for networks which output sparse point clouds. We further show that by training point networks with GAMesh, we can directly optimize the vertex positions to generate adaptive meshes with arbitrary topologies.
Comment: Accepted to 3DV 2020
Databáze: OpenAIRE
Externí odkaz: