CorresNeRF: Image Correspondence Priors for Neural Radiance Fields
NeurIPS 2023(2023)
摘要
Neural Radiance Fields (NeRFs) have achieved impressive results in novel view
synthesis and surface reconstruction tasks. However, their performance suffers
under challenging scenarios with sparse input views. We present CorresNeRF, a
novel method that leverages image correspondence priors computed by
off-the-shelf methods to supervise NeRF training. We design adaptive processes
for augmentation and filtering to generate dense and high-quality
correspondences. The correspondences are then used to regularize NeRF training
via the correspondence pixel reprojection and depth loss terms. We evaluate our
methods on novel view synthesis and surface reconstruction tasks with
density-based and SDF-based NeRF models on different datasets. Our method
outperforms previous methods in both photometric and geometric metrics. We show
that this simple yet effective technique of using correspondence priors can be
applied as a plug-and-play module across different NeRF variants. The project
page is at https://yxlao.github.io/corres-nerf.
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