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 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.

arXiv commentAccepted to 3DV 2020

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• Learning Representations and Generative Models for 3D Point Clouds
• Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
• Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
• Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
• Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning
• Learning Part Boundaries from 3D Point Clouds
• GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects