MLP-Stereo: Heterogeneous Feature Fusion in MLP for Stereo Matching
Shuiqiang Ye, Pengcheng Zeng, Pengfei Li, Weiqi Wang, Wang Xin-an, Yong Jun Zhao · 2022 IEEE International Conference on Image Processing (ICIP) · 2022
CNNs’ strong inductive biases of locality and weight sharing provide powerful representation ability and data sample utilization efficiency. However, the weight sharing might smooth out the discrepancy between similar pixels, resulting in the wrong matching between left and right camera-image pair in thin structures region and repetitive texture region. In this paper, we propose a novel Heterogeneous Feature Fusion in MLP (HFF-MLP) for Stereo matching. It employs MLP structure and relaxes the weights sharing in the local spatial region. To this end, pixels in thin structures region and repetitive texture region are dealt with independently using the exclusive weights. Based on HFF -MLP module, we design a real-time network, i.e., MLP-Stereo. Experimental results show that our proposed HFF-MLP achieves competitive results on KITTI 2015 test dataset with the running time of 46 milliseconds. Furthermore, it performs much better than other real-time network in thin structures region and repetitive texture region.