  Volumetric neural rendering methods like NeRF generate high-quality view
synthesis results but are optimized per-scene leading to prohibitive
reconstruction time. On the other hand, deep multi-view stereo methods can
quickly reconstruct scene geometry via direct network inference. Point-NeRF
combines the advantages of these two approaches by using neural 3D point
clouds, with associated neural features, to model a radiance field. Point-NeRF
can be rendered efficiently by aggregating neural point features near scene
surfaces, in a ray marching-based rendering pipeline. Moreover, Point-NeRF can
be initialized via direct inference of a pre-trained deep network to produce a
neural point cloud; this point cloud can be finetuned to surpass the visual
quality of NeRF with 30X faster training time. Point-NeRF can be combined with
other 3D reconstruction methods and handles the errors and outliers in such
methods via a novel pruning and growing mechanism. The experiments on the DTU,
the NeRF Synthetics , the ScanNet and the Tanks and Temples datasets
demonstrate Point-NeRF can surpass the existing methods and achieve the
state-of-the-art results.
