Softmesh: Learning Probabilistic Mesh Connectivity via Image Supervision


Eric-Tuan LE, Niloy Mitra and Iasonas Kokkinos


In this work we introduce Softmesh, a fully differentiable pipeline to transform a 3D point cloud into a probabilistic mesh representation that allows us to directly 2D images. We use this pipeline to learn point connectivity from only 2D rendering supervision, reducing the supervision requirements for mesh-based representations. We evaluate our approach in a set of rendering tasks, including silhouette, normal, and depth rendering on both rigid and non-rigid objects. We introduce transfer learning approaches to handle the diversity of the task requirements, and also explore the potential of learning across categories. We demonstrate that SoftMesh achieves competitive performance even against methods trained with full mesh supervision.

PDF (protected)

  Important Dates

All deadlines are 23:59 Pacific Time (PT). No extensions will be granted.

Paper registration July 23 30, 2021
Paper submission July 30, 2021
Supplementary August 8, 2021
Tutorial submission August 15, 2021
Tutorial notification August 31, 2021
Rebuttal period September 16-22, 2021
Paper notification October 1, 2021
Camera ready October 15, 2021
Demo submission July 30 Nov 15, 2021
Demo notification Oct 1 Nov 19, 2021
Tutorial November 30, 2021
Main conference December 1-3, 2021