TY - EJOU AU - Yang, Xin AU - Chang, Qingling AU - Xu, Shiting AU - Liu, Xinlin AU - Cui, Yan TI - Monocular Depth Estimation with Sharp Boundary T2 - Computer Modeling in Engineering \& Sciences PY - 2023 VL - 136 IS - 1 SN - 1526-1506 AB - Monocular depth estimation is the basic task in computer vision. Its accuracy has tremendous improvement in the decade with the development of deep learning. However, the blurry boundary in the depth map is a serious problem. Researchers find that the blurry boundary is mainly caused by two factors. First, the low-level features, containing boundary and structure information, may be lost in deep networks during the convolution process. Second, the model ignores the errors introduced by the boundary area due to the few portions of the boundary area in the whole area, during the backpropagation. Focusing on the factors mentioned above. Two countermeasures are proposed to mitigate the boundary blur problem. Firstly, we design a scene understanding module and scale transform module to build a lightweight fuse feature pyramid, which can deal with low-level feature loss effectively. Secondly, we propose a boundary-aware depth loss function to pay attention to the effects of the boundary’s depth value. Extensive experiments show that our method can predict the depth maps with clearer boundaries, and the performance of the depth accuracy based on NYU-Depth V2, SUN RGB-D, and iBims-1 are competitive. KW - Monocular depth estimation; object boundary; blurry boundary; scene global information; feature fusion; scale transform; boundary aware DO - 10.32604/cmes.2023.023424