Tetra-NeRF: Representing Neural Radiance Fields Using Tetrahedra

• URL: http://arxiv.org/abs/2304.09987v3
• Date: Sun, 20 Aug 2023 07:25:50 GMT
• Title: Tetra-NeRF: Representing Neural Radiance Fields Using Tetrahedra
• Authors: Jonas Kulhanek and Torsten Sattler
• Abstract summary: This paper proposes to use an adaptive representation based on tetrahedra obtained by Delaunay instead of uniform subdivision or point-based representations. We show that such a representation enables efficient training and leads to state-of-the-art results.
• Score: 31.654710376807593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Radiance Fields (NeRFs) are a very recent and very popular approach for the problems of novel view synthesis and 3D reconstruction. A popular scene representation used by NeRFs is to combine a uniform, voxel-based subdivision of the scene with an MLP. Based on the observation that a (sparse) point cloud of the scene is often available, this paper proposes to use an adaptive representation based on tetrahedra obtained by Delaunay triangulation instead of uniform subdivision or point-based representations. We show that such a representation enables efficient training and leads to state-of-the-art results. Our approach elegantly combines concepts from 3D geometry processing, triangle-based rendering, and modern neural radiance fields. Compared to voxel-based representations, ours provides more detail around parts of the scene likely to be close to the surface. Compared to point-based representations, our approach achieves better performance. The source code is publicly available at: https://jkulhanek.com/tetra-nerf.

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