GEDepth: Ground Embedding for Monocular Depth Estimation

TL;DR

GEDepth introduces a ground embedding module that decouples camera parameters from pictorial cues, significantly enhancing the generalization of monocular depth estimation across diverse real-world scenarios.

Contribution

The paper proposes a novel, lightweight ground embedding module that improves generalization in monocular depth estimation by decoupling camera parameters from image cues.

Findings

Achieves state-of-the-art results on popular benchmarks.

Significantly improves cross-domain generalization.

Flexible and lightweight module can be integrated into various networks.

Abstract

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 limiting their generalizability in real-world scenarios. To cope with this challenge, this paper proposes a novel ground embedding module to decouple camera parameters from pictorial cues, thus promoting the generalization capability. Given camera parameters, the proposed 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 ground depth with residual depth. Our ground embedding 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 generalization improvement on a wide range of cross-domain tests.