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 the Delaunay triangulation instead of the 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.

翻译：神经辐射场（NeRFs）是近年来在新视角合成与三维重建领域中极为流行的方法。NeRF常用的一种场景表示方式是将均匀的体素细分结构与多层感知机（MLP）相结合。基于场景（稀疏）点云通常可获取这一观察，本文提出采用由德劳内三角剖分得到的四面体自适应表示方法，替代传统的均匀细分或点云表示。我们证明，这种表示方法能够实现高效训练，并取得最先进的性能表现。本方法巧妙融合了三维几何处理、基于三角面的渲染技术以及现代神经辐射场等概念。与基于体素的表示相比，本方法能在靠近物体表面的场景区域提供更丰富的细节；与基于点的表示相比，本方法则实现了更优的性能。