Learning Delaunay Surface Elements for Mesh Reconstruction

TL;DR

This paper introduces a novel mesh reconstruction method using learned Delaunay surface elements that produce more manifold meshes from point clouds, improving over existing approaches.

Contribution

The method leverages 2D Delaunay triangulations of local geodesic neighborhoods to ensure manifold surface reconstruction from point clouds.

Findings

Achieves higher manifoldness in reconstructed meshes.

Outperforms existing methods in handling arbitrary topology.

Provides publicly available code and models.

Abstract

We present a method for reconstructing triangle meshes from point clouds. Existing learning-based methods for mesh reconstruction mostly generate triangles individually, making it hard to create manifold meshes. We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements. Our method first estimates local geodesic neighborhoods around each point. We then perform a 2D projection of these neighborhoods using a learned logarithmic map. A Delaunay triangulation in this 2D domain is guaranteed to produce a manifold patch, which we call a Delaunay surface element. We synchronize the local 2D projections of neighboring elements to maximize the manifoldness of the reconstructed mesh. Our results show that we achieve better overall manifoldness of our reconstructed meshes than current methods to reconstruct meshes with arbitrary topology. Our code, data and pretrained models can be found online: https://github.com/mrakotosaon/dse-meshing