FGDepth: Fine-Grained Boundary Perception Enhancement in Self-Supervised Indoor Depth Estimation

Self-supervised depth estimation has been widely ap plied in indoor environments. However, the presence of numerous objects and complex structural boundaries often leads existing methods to generate blurred or imprecise depth edges. To address this challenge, we propose FGDepth, a framework designed to enhance depth estimation through fine-grained boundary perception. Firstly, we introduce an SR (Super Resolution) auxiliary training branch that shares feature layers with the encoder of the depth estimation network. By utilizing the powerful detail recovery capabilities of the SR task, we improve the depth network's sensitivity to indoor object boundaries. Notably, the SR branch is used only during training, ensuring no added computational cost during inference. As far as we know, we are the first to employ the SR task as an auxiliary method for indoor self-supervised depth estimation. Second, we observe that outdoor scenes display significant depth variations due to their broader depth range, whereas indoor scenes typically lack clear boundary distinctions in background areas. This is because distant objects in indoor settings often appear as continuous surfaces with similar depths. This characteristic necessitates careful mitigation of background depth uniformity interference when enhancing depth boundaries in indoor scenes. Therefore, we design a depth-adaptive object mask to provide target object boundary information and use a triplet loss to align these differences with the depth map. Experimental results on three benchmark datasets show that our method outperforms existing approaches. We also conduct ablation studies to validate the contributions of each component.