基于多层级联循环的立体匹配网络.

To improve the problem that existing stereo matching algorithms have difficulty in maintaining a good balance between higher parallax estimation accuracy and faster model inference speed, this paper proposed an efficient and accurate multi-layer cascaded recurrent stereo-matching network. Firstly, this paper designed a multilayer network to introduce position encoding and self-attention mechanisms on the higher resolution feature maps, then updated the disparity values using the disparity refinement strategies of hierarchical recurrent refinement, cascaded refinement, and recurrent refinement to recover the detailed information of stereo images better. In addition, this paper improved the method of disparity iterative update by using a light- weight group correlation layer at a low scale and an adaptive group correlation layer at a large scale to update the disparity values, which reduced the computational effort of parallax iterative update and improved the inference speed of the model. The experimental results show that the proposed algorithm has a faster model inference speed than others while achieving a competitive parallax estimation accuracy. [ABSTRACT FROM AUTHOR]