GAM-Depth: Self-Supervised Indoor Depth Estimation Leveraging a Gradient-Aware Mask and Semantic Constraints
Anqi Cheng,Zhiyuan Yang,Haiyue Zhu,Kezhi Mao,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 co...
