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.

翻译：神经辐射场（NeRFs）是近期在新型视角合成和三维重建问题中非常流行的方法。NeRFs常用的一种场景表示是将均匀的基于体素的场景细分与MLP相结合。基于场景的（稀疏）点云通常可用的观察，本文提出使用由德劳内三角剖分获得的四面体构成的自适应表示，替代均匀细分或基于点的表示。我们证明这种表示能够实现高效训练，并取得最先进的结果。我们的方法优雅地结合了三维几何处理、基于三角形的渲染和现代神经辐射场的概念。与基于体素的表示相比，我们的方法在靠近表面区域的场景部分提供了更多细节；与基于点的表示相比，我们的方法实现了更好的性能。源代码公开于：https://jkulhanek.com/tetra-nerf。