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.

翻译：神经辐射场（NeRF）是一种新兴的视图合成方法，通过对三维空间中的点进行采样并估计其存在概率和颜色概率。NeRF的缺点在于需要采样大量三维点，因此训练时间较长。此外，若从遮挡区域或物体不可能存在的空间采样点，NeRF的渲染质量会下降。这些问题可通过估计三维场景的几何结构来解决。本文提出一种近表面采样框架以提升NeRF的渲染质量。为此，所提出方法利用训练集的深度图像估计三维物体的表面，并仅在此表面附近进行采样。为获取新视角的深度信息，本文提出一种三维点云生成方法及基于点云投影深度的简单优化方法。实验结果表明，与原始NeRF及最先进的基于深度的NeRF方法相比，所提出的近表面采样NeRF框架能显著提升渲染质量。此外，采用所提出的近表面采样框架可大幅加速NeRF模型的训练时间。