Data Augmentation Based on Depth Information for Neural Radiance Fields

Abstract

Abstract Neural Radiance Fields (NeRF) have attracted particular attention due to their exceptional capability in virtual view generation from a sparse set of input images. However, their scope is constrained by the substantial amount of images required for training. This work introduces a data augmentation methodology to train NeRF using external depth information. The approach entails generating new virtual images at different positions through the utilization of MPEG's reference view synthesizer (RVS) to augment the training image pool for NeRF. Results demonstrate a substantial enhancement in the output quality when employing the generated views in comparison to a scenario where they are omitted.