NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion
This work addresses depth perception challenges in computer vision for applications like robotics and autonomous driving, representing an incremental improvement over existing methods.
The paper tackles monocular depth estimation and completion by introducing a physics-driven framework that estimates surface normal and distance maps as intermediate outputs, achieving state-of-the-art performance on datasets like NYU-Depth-v2, KITTI, and SUN RGB-D.
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.