Digging into Depth Priors for Outdoor Neural Radiance Fields
Chen Wang Jiadai Sun Lina Liu Chenming Wu Zhelun Shen Dayan Wu Yuchao Dai Liangjun Zhang
Neural Radiance Fields (NeRF) have demonstrated impressive performance in
vision and graphics tasks, such as novel view synthesis and immersive reality.
However, the shape-radiance ambiguity of radiance fields remains a challenge,
especially in the sparse viewpoints setting. Recent work resorts to integrating
depth priors into outdoor NeRF training to alleviate the issue. However, the
criteria for selecting depth priors and the relative merits of different priors
have not been thoroughly investigated. Moreover, the relative merits of
selecting different approaches to use the depth priors is also an unexplored
problem. In this paper, we provide a comprehensive study and evaluation of
employing depth priors to outdoor neural radiance fields, covering common depth
sensing technologies and most application ways. Specifically, we conduct
extensive experiments with two representative NeRF methods equipped with four
commonly-used depth priors and different depth usages on two widely used
outdoor datasets. Our experimental results reveal several interesting findings
that can potentially benefit practitioners and researchers in training their
NeRF models with depth priors. Project Page:
https://cwchenwang.github.io/outdoor-nerf-depth