  We represent the ResNeRF, a novel geometry-guided two-stage framework for
indoor scene novel view synthesis. Be aware of that a good geometry would
greatly boost the performance of novel view synthesis, and to avoid the
geometry ambiguity issue, we propose to characterize the density distribution
of the scene based on a base density estimated from scene geometry and a
residual density parameterized by the geometry. In the first stage, we focus on
geometry reconstruction based on SDF representation, which would lead to a good
geometry surface of the scene and also a sharp density. In the second stage,
the residual density is learned based on the SDF learned in the first stage for
encoding more details about the appearance. In this way, our method can better
learn the density distribution with the geometry prior for high-fidelity novel
view synthesis while preserving the 3D structures. Experiments on large-scale
indoor scenes with many less-observed and textureless areas show that with the
good 3D surface, our method achieves state-of-the-art performance for novel
view synthesis.
