Guided Depth Super-Resolution (GDSR) enhances low-resolution (LR) depth maps by leveraging high-resolution (HR) color images. The primary challenges involve achieving effective cross-modal data alignment and fusion, as well as incorporating multi-scale information within the Transformer architecture. To address these challenges, we propose a novel network architecture named DRMPNet which integrates two key components: Offset-based Detail Refinement (ODR) and Structure-guided Multi-scale Perception (SMP). ODR leverages offset calibration and window cross-attention to align and fuse LR depth maps with color images, effectively recovering local depth details. Meanwhile, SMP employs a structure generator and multi-scale cross-attention to capture scene details and structures at multiple scales, thereby enhancing the network’s contextual understanding. Extensive experiments on various benchmark datasets demonstrate the effectiveness of our method.

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.