Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos

Abstract

The success of the Neural Radiance Fields (NeRFs) for modeling and free-view rendering static objects has in-spired numerous attempts on dynamic scenes. Current techniques that utilize neural rendering for facilitating free-view videos (FVVs) are restricted to either offline rendering or are capable of processing only brief sequences with minimal motion. In this paper, we present a novel technique, Residual Radiance Field or ReRF, as a highly com-pact neural representation to achieve real-time FVV ren-dering on long-duration dynamic scenes. ReRF explicitly models the residual information between adjacent times-tamps in the spatial-temporal feature space, with a global coordinate-based tiny MLP as the feature decoder. Specif-ically, ReRF employs a compact motion grid along with a residual feature grid to exploit inter-frame feature similar-ities. We show such a strategy can handle large motions without sacrificing quality. We further present a sequential training scheme to maintain the smoothness and the spar-sity of the motion/residual grids. Based on ReRF, we design a special FVV codec that achieves three orders of magni-tudes compression rate and provides a companion ReRF player to support online streaming of long-duration FVVs of dynamic scenes. Extensive experiments demonstrate the effectiveness of ReRF for compactly representing dynamic radiance fields, enabling an unprecedented free-viewpoint viewing experience in speed and quality.