Depth assisted novel view synthesis using few images

Abstract

In this paper, we introduce a novel approach to improve the performance of Neural Radiance Fields (NeRF) from limited input views. NeRF has exhibited impressive capabilities in producing photo-realistic renderings when trained on dense input views, but its performance degrades as the number of training views decreases. Our key insight is that the original NeRF lacks geometric regularization and appearance information due to limited inputs, resulting in an over-fitting issue. To address this challenge, we present a novel method: first, a global sampling method with geometric regularization is employed by utilizing warped images as additional pseudo-views, which optimizes the multi-view consistency during the training. Second, we introduce a local patch sampling technique with perceptual regularization to ensure pixel correspondence in appearance. Furthermore, we incorporate depth information for explicit geometry regularization. We evaluate our method on the DTU dataset and LLFF dataset from a different number of inputs. Extensive evaluations demonstrate that our approach outperforms existing benchmarks across various metrics, achieving state-of-the-art results.