Efficient Ray Sampling for Radiance Fields Reconstruction

TL;DR

This paper introduces a novel ray sampling method for neural radiance fields that accelerates training and improves rendering quality by focusing on informative pixel regions, reducing redundancy.

Contribution

It proposes a guided sampling strategy based on pixel and depth variation, enhancing training efficiency and rendering accuracy in NeRF models.

Findings

Faster convergence of NeRF training.

Improved rendering quality in complex regions.

Outperforms state-of-the-art methods on benchmarks.

Abstract

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.