GAM-Depth: Self-Supervised Indoor Depth Estimation Leveraging a
  Gradient-Aware Mask and Semantic Constraints
Anqi Cheng Zhiyuan Yang Haiyue Zhu Kezhi Mao
Self-supervised depth estimation has evolved into an image reconstruction
task that minimizes a photometric loss. While recent methods have made strides
in indoor depth estimation, they often produce inconsistent depth estimation in
textureless areas and unsatisfactory depth discrepancies at object boundaries.
To address these issues, in this work, we propose GAM-Depth, developed upon two
novel components: gradient-aware mask and semantic constraints. The
gradient-aware mask enables adaptive and robust supervision for both key areas
and textureless regions by allocating weights based on gradient magnitudes.The
incorporation of semantic constraints for indoor self-supervised depth
estimation improves depth discrepancies at object boundaries, leveraging a
co-optimization network and proxy semantic labels derived from a pretrained
segmentation model. Experimental studies on three indoor datasets, including
NYUv2, ScanNet, and InteriorNet, show that GAM-Depth outperforms existing
methods and achieves state-of-the-art performance, signifying a meaningful step
forward in indoor depth estimation. Our code will be available at
https://github.com/AnqiCheng1234/GAM-Depth.