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arXiv Computer Vision By Mengtan Zhang, Yuchuan Cui, Yu Ma, Zizhan Guo, Wei Ye, Rui Fan

Learning to Discriminate Scene Structures Makes Self-Supervised Depth Learning Scalable

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The paper introduces MaSS, a framework that adds a dedicated structural representation pathway to self‑supervised monocular depth estimation. By using a scene‑structure discrimination objective, MaSS learns generalized, layout‑aware structural representations that decouple from contextual features. Experiments on four diverse datasets show MaSS outperforms prior methods and benefits from mixed‑domain training, while zero‑shot tests on eight additional datasets confirm its strong generalizability to out‑of‑distribution scenes.

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