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About this paper
Runze Chen, Haiyong Luo, Fang Zhao, Jingze Yu, Yupeng Jia, Juan Wang, Xuepeng Ma
Researchers developed Scent-Depth, a structure-centric approach for robust monocular depth estimation that employs knowledge distillation and image decomposition. This method achieves state-of-the-art performance on out-of-distribution datasets featuring adverse weather, motion blur, and nighttime conditions, without requiring training on corrupted data.
Key insights
- Robust monocular depth estimation can be achieved by fundamentally improving a model's understanding of scene structure, moving beyond heavy reliance on local textures.
- Explicitly disentangling structural features from superficial textures, coupled with transferring high-level semantic knowledge, significantly enhances model robustness and generalization.
- Graph Isomorphism Networks can effectively facilitate the transfer of robust semantic-structural correlations from a pre-trained expert to a depth estimation student model.
Summary by alphaXiv.
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