A Self-supervised Monocular Depth Estimation Algorithm Based on Local Attention and Iterative Pose Refinement

‍ ‍Self-supervised monocular depth estimation is widely used in many areas， such as autonomous driving and intelligent manufacturing. However， due to the large amount of training noise in self-supervised training， the accuracy of self-supervised monocular depth estimation is limited. To improve the performance of self-supervised monocular depth estimation algorithm， we proposed a modified self-supervised monocular depth estimation algorithm based on local attention mechanism and iterative pose refinement. First， for the depth estimation network， we proposed a local attention mechanism， which is based on the high correlation between the depth of pixels in a local patch， to fuse features of high-resolution feature map. Second， for the pose estimation network， we proposed an iterative refinement based architecture， which decreases the pose estimation difficulty with residual optimization and improves the pose estimation accuracy to benefit the depth estimation network. Experiments shown that， the proposed modified self-supervised monocular depth estimation algorithm significantly improves the depth estimation accuracy.