  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.
