NDDepth: Normal-Distance Assisted Monocular Depth Estimation and
  Completion
Shuwei Shao Zhongcai Pei Weihai Chen Peter C. Y. Chen Zhengguo Li
Over the past few years, monocular depth estimation and completion have been
paid more and more attention from the computer vision community because of
their widespread applications. In this paper, we introduce novel physics
(geometry)-driven deep learning frameworks for these two tasks by assuming that
3D scenes are constituted with piece-wise planes. Instead of directly
estimating the depth map or completing the sparse depth map, we propose to
estimate the surface normal and plane-to-origin distance maps or complete the
sparse surface normal and distance maps as intermediate outputs. To this end,
we develop a normal-distance head that outputs pixel-level surface normal and
distance. Meanwhile, the surface normal and distance maps are regularized by a
developed plane-aware consistency constraint, which are then transformed into
depth maps. Furthermore, we integrate an additional depth head to strengthen
the robustness of the proposed frameworks. Extensive experiments on the
NYU-Depth-v2, KITTI and SUN RGB-D datasets demonstrate that our method exceeds
in performance prior state-of-the-art monocular depth estimation and completion
competitors. The source code will be available at
https://github.com/ShuweiShao/NDDepth.