MaSS: Improving Cross-Domain Scalability of Self-Supervised Monocular Depth Estimation via Scene Structure Discrimination

The MaSS framework proposes a structural representation pathway explicitly decoupled from contextual features, using a new scene-structure discrimination objective to learn layout-aware structural representations usable for depth decoding. Trained individually and jointly on four diverse datasets, MaSS significantly outperforms prior self-supervised methods and becomes the first method to consistently benefit from mixed-domain training; zero-shot evaluation on eight additional datasets further demonstrates its generalizability, especially in entirely out-of-distribution scenes. Source code: https://mias.group/MaSS.