Neural radiance field (NeRF) is an emerging view synthesis method that
samples points in a three-dimensional (3D) space and estimates their existence
and color probabilities. The disadvantage of NeRF is that it requires a long
training time since it samples many 3D points. In addition, if one samples
points from occluded regions or in the space where an object is unlikely to
exist, the rendering quality of NeRF can be degraded. These issues can be
solved by estimating the geometry of 3D scene. This paper proposes a
near-surface sampling framework to improve the rendering quality of NeRF. To
this end, the proposed method estimates the surface of a 3D object using depth
images of the training set and sampling is performed around there only. To
obtain depth information on a novel view, the paper proposes a 3D point cloud
generation method and a simple refining method for projected depth from a point
cloud. Experimental results show that the proposed near-surface sampling NeRF
framework can significantly improve the rendering quality, compared to the
original NeRF and a state-of-the-art depth-based NeRF method. In addition, one
can significantly accelerate the training time of a NeRF model with the
proposed near-surface sampling framework.

该论文提出了一种改进 NeRF 渲染质量的近表面采样框架，通过估计 3D 场景的表面，只在表面周围进行采样，从而提高了渲染质量，并且能够显著加速 NeRF 模型的训练时间。