Learning to Discriminate Scene Structures Makes Self-Supervised Depth Learning Scalable
Self-supervised monocular depth estimation is appealing for its potential to scale with unlabeled data, yet existing methods often degrade when trained on diverse scenes. This study finds that, under self-supervision, models tend to encode dataset-specific structural biases rather than transferable scene geometry, leading to cross-domain optimization conflicts. To address this issue, this study proposes the MaSS framework, aiming to make self-supervised depth learning scalable to diverse data. MaSS introduces a dedicated structural representation pathway explicitly decoupled from contextual features, which is guided by a novel scene-structure discrimination objective to learn generalized,...
| Authors | Mengtan Zhang, Yuchuan Cui, Yu Ma et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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