MonoPatchNeRF: Improving Neural Radiance Fields with Patch-based
  Monocular Guidance
Yuqun Wu Jae Yong Lee Chuhang Zou Shenlong Wang Derek Hoiem
The latest regularized Neural Radiance Field (NeRF) approaches produce poor
geometry and view extrapolation for multiview stereo (MVS) benchmarks such as
ETH3D. In this paper, we aim to create 3D models that provide accurate geometry
and view synthesis, partially closing the large geometric performance gap
between NeRF and traditional MVS methods. We propose a patch-based approach
that effectively leverages monocular surface normal and relative depth
predictions. The patch-based ray sampling also enables the appearance
regularization of normalized cross-correlation (NCC) and structural similarity
(SSIM) between randomly sampled virtual and training views. We further show
that "density restrictions" based on sparse structure-from-motion points can
help greatly improve geometric accuracy with a slight drop in novel view
synthesis metrics. Our experiments show 4x the performance of RegNeRF and 8x
that of FreeNeRF on average F1@2cm for ETH3D MVS benchmark, suggesting a
fruitful research direction to improve the geometric accuracy of NeRF-based
models, and sheds light on a potential future approach to enable NeRF-based
optimization to eventually outperform traditional MVS.