  In this work, we present a new multi-view depth estimation method that
utilizes both conventional reconstruction and learning-based priors over the
recently proposed neural radiance fields (NeRF). Unlike existing neural network
based optimization method that relies on estimated correspondences, our method
directly optimizes over implicit volumes, eliminating the challenging step of
matching pixels in indoor scenes. The key to our approach is to utilize the
learning-based priors to guide the optimization process of NeRF. Our system
firstly adapts a monocular depth network over the target scene by finetuning on
its sparse SfM+MVS reconstruction from COLMAP. Then, we show that the
shape-radiance ambiguity of NeRF still exists in indoor environments and
propose to address the issue by employing the adapted depth priors to monitor
the sampling process of volume rendering. Finally, a per-pixel confidence map
acquired by error computation on the rendered image can be used to further
improve the depth quality. Experiments show that our proposed framework
significantly outperforms state-of-the-art methods on indoor scenes, with
surprising findings presented on the effectiveness of correspondence-based
optimization and NeRF-based optimization over the adapted depth priors. In
addition, we show that the guided optimization scheme does not sacrifice the
original synthesis capability of neural radiance fields, improving the
rendering quality on both seen and novel views. Code is available at
https://github.com/weiyithu/NerfingMVS.
