Semantics-Driven Contrastive Learning for Real-World Depth Super Resolution

Low-resolution (LR) depth maps captured by depth sensors often suffer from structural distortions, noise, and blurring, limiting their practical usability. While most existing depth super-resolution (DSR) methods rely on synthetic datasets, they fail to accurately model real-world degradations, leading to poor performance on real-world data. To address this limitation, we identify two key challenges in the real-world DSR task: structural contour inconsistency and regional degradation inconsistency. The former arises from structural distortions in LR depth maps, while the latter stems from varying degradation levels in smooth regions. Upon this, we propose a Semantics-Driven Contrastive Learning (SDCL) pipeline for real-world DSR, leveraging semantic priors from the SAM model to enhance structural contour reconstruction and region-wise degradation handling. We introduce two novel contrastive loss functions: Structural Contour Alignment (SCA) loss, which aligns depth contours with semantic boundaries, and Regional Degradation Discrimination (RDD) loss, which optimizes smooth region restoration through region-level contrastive learning. Our approach is model-agnostic and can be seamlessly integrated into existing DSR frameworks. Experiments demonstrate that our method significantly enhances DSR performance on real-world DSR datasets.