Exploiting Depth Priors for Few-Shot Neural Radiance Field Reconstruction

Abstract

The performance of neural radiance field technologies deteriorates rapidly when sparse views are used as input. In this paper, we propose a simulated viewpoint enhancement for surface reconstruction that extracts diverse geometric features from the depth to address this limitation. We design a novel pseudo-view generation approach to simulate dense viewpoints, in which the RGB-D pairs are converted into new images corresponding to the translational poses through reprojection. To address potential holes in the pseudo-views resulting from occlusion in the reprojecting process, we propose a hole handling mechanism to exclude them during training. Additionally, normal information is derived from the depth, serving as supervision to enhance the scene geometry. Experiments conducted on various baseline models and datasets demonstrate that our algorithm effectively utilizes the inherent geometry embedded in the depth maps, leading to a significant improvement in the quality of 3D surface reconstruction compared to existing methods.