GEDepth: Ground Embedding for Monocular Depth Estimation

Xiaodong Yang, Zhuang Ma, Zhiyu Ji, Zhe Ren; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 12719-12727

Monocular depth estimation is an ill-posed problem as the same 2D image can be projected from infinite 3D scenes. Although the leading algorithms in this field have reported significant improvement, they are essentially geared to the particular compound of pictorial observations and camera parameters (i.e., intrinsics and extrinsics), strongly limit- ing their generalizability in real-world scenarios. In or- der to cope with this difficulty, this paper proposes a novel ground embedding module to decouple camera parameters from pictorial cues, thus promoting the generalization ca- pability. Given camera parameters, our module generates the ground depth, which is stacked with the input image and referenced in the final depth prediction. A ground attention is designed in the module to optimally combine the ground depth with the residual depth. The proposed ground embed- ding is highly flexible and lightweight, leading to a plug-in module that is amenable to be integrated into various depth estimation networks. Experiments reveal that our approach achieves the state-of-the-art results on popular benchmarks, and more importantly, renders significant improvement on the cross-domain generalization.