Point-NeRF models radiance fields using neural 3D point clouds with associated features, enabling efficient rendering via ray marching. It can be initialized via deep network inference and finetuned to surpass standard NeRF quality with 30x faster training. The method includes a pruning and growing mechanism to handle errors from external 3D reconstruction methods.