GAMesh: Guided and Augmented Meshing for Deep Point Networks
Abstract
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 singleview 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.
 Publication:

arXiv eprints
 Pub Date:
 October 2020
 DOI:
 10.48550/arXiv.2010.09774
 arXiv:
 arXiv:2010.09774
 Bibcode:
 2020arXiv201009774A
 Keywords:

 Computer Science  Computer Vision and Pattern Recognition;
 Computer Science  Computational Geometry;
 Computer Science  Graphics;
 Computer Science  Machine Learning
 EPrint:
 Accepted to 3DV 2020