  Accelerating neural radiance fields training is of substantial practical
value, as the ray sampling strategy profoundly impacts network convergence.
More efficient ray sampling can thus directly enhance existing NeRF models'
training efficiency. We therefore propose a novel ray sampling approach for
neural radiance fields that improves training efficiency while retaining
photorealistic rendering results. First, we analyze the relationship between
the pixel loss distribution of sampled rays and rendering quality. This reveals
redundancy in the original NeRF's uniform ray sampling. Guided by this finding,
we develop a sampling method leveraging pixel regions and depth boundaries. Our
main idea is to sample fewer rays in training views, yet with each ray more
informative for scene fitting. Sampling probability increases in pixel areas
exhibiting significant color and depth variation, greatly reducing wasteful
rays from other regions without sacrificing precision. Through this method, not
only can the convergence of the network be accelerated, but the spatial
geometry of a scene can also be perceived more accurately. Rendering outputs
are enhanced, especially for texture-complex regions. Experiments demonstrate
that our method significantly outperforms state-of-the-art techniques on public
benchmark datasets.
