Neural Geometry Fields for Meshes

Abstract

Recent work on using neural fields to represent surfaces has resulted in significant improvements in representational capability and computational efficiency. However, to our knowledge, most existing work has focused on implicit representations such as signed distance fields or volumes, and little work has explored their application to discrete surface geometry, i.e., 3D meshes, limiting the applicability of neural surface representations.