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 this https URL

使用图像对应先验来监督神经辐射场的训练，生成稠密高质量的对应关系，并将其用于规范化神经辐射场的训练，从而在新视角合成和表面重建任务中优于先前的方法。