Learning Deformable Tetrahedral Meshes for 3D Reconstruction

TL;DR

This paper introduces Deformable Tetrahedral Meshes (DefTet), a novel volumetric representation for 3D reconstruction that is differentiable, efficient, and capable of high-fidelity, complex topology reconstructions from noisy point clouds and single images.

Contribution

DefTet is a new volumetric tetrahedral mesh parameterization optimized for neural 3D reconstruction, enabling high-quality, efficient, and topology-agnostic 3D shape recovery.

Findings

Achieves high-fidelity 3D reconstructions with smaller grids.

Handles complex topologies without post-processing.

Produces high-quality meshes from noisy point clouds and single images.

Abstract

3D shape representations that accommodate learning-based 3D reconstruction are an open problem in machine learning and computer graphics. Previous work on neural 3D reconstruction demonstrated benefits, but also limitations, of point cloud, voxel, surface mesh, and implicit function representations. We introduce Deformable Tetrahedral Meshes (DefTet) as a particular parameterization that utilizes volumetric tetrahedral meshes for the reconstruction problem. Unlike existing volumetric approaches, DefTet optimizes for both vertex placement and occupancy, and is differentiable with respect to standard 3D reconstruction loss functions. It is thus simultaneously high-precision, volumetric, and amenable to learning-based neural architectures. We show that it can represent arbitrary, complex topology, is both memory and computationally efficient, and can produce high-fidelity reconstructions with a significantly smaller grid size than alternative volumetric approaches. The predicted surfaces are also inherently defined as tetrahedral meshes, thus do not require post-processing. We demonstrate that DefTet matches or exceeds both the quality of the previous best approaches and the performance of the fastest ones. Our approach obtains high-quality tetrahedral meshes computed directly from noisy point clouds, and is the first to showcase high-quality 3D tet-mesh results using only a single image as input. Our project webpage: https://nv-tlabs.github.io/DefTet/