Shao, S., Pei, Z., Chen, W., Chen, P. C. Y., & Li, Z. (2024). NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12), 8883–8899. https://doi.org/10.1109/tpami.2024.3411571
Abstract:
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 NYUDepth-
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Manufacturing, Trade, and Connectivity Programmatic Funds
Grant Reference no. : M23L7b0021