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 and a Delaunay representation 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（NeRF）是近期在新型视图合成与三维重建问题中非常流行的方法。NeRF常用的一种场景表示方式是将基于均匀体素的场景细分与多层感知机（MLP）结合。基于场景的（稀疏）点云通常可获取这一观察，本文提出采用基于四面体的自适应表示及Delaunay表示，以替代均匀细分或基于点的表示方法。我们证明，这种表示方法能够实现高效训练，并取得当前最优结果。我们的方法优雅地融合了三维几何处理、基于三角形的渲染以及现代神经辐射场的概念。与基于体素的表示相比，我们的方法能在可能接近场景表面的区域提供更多细节；与基于点的表示相比，我们的方法实现了更优的性能。