关键词: 自监督, 单目深度估计, 边界伪影, 最小化光度重投影函数, 自动掩蔽损失

Abstract:

Due to the occlusion and motion between objects, the estimated depth maps will be blurred and appeared boundary artifacts using current self-supervised monocular depth estimation methods. To address the above problems, an anti-occlusion monocular depth estimation algorithm is proposed by designing the loss function. The proposed algorithm, ignoring the occluded pixels with higher loss, uses the minimized photometric re-projection function to match the minimum error between two adjacent frames of the target image. Moreover, the automatic masking loss is used to process the boundary artifacts caused by object movement. Finally, the comparison of experimental results on the KITTI dataset shows that the depth maps estimated by the proposed algorithm are clearer, and the boundary artifacts in these depth maps are also reduced.

Key words: self-supervised, monocular depth estimation, boundary artifacts, minimization of photometric reprojection, automatic masking loss