Region Segmentation-Based Dual Branch Cost Aggregation for Lightweight Stereo Matching

Recently, stereo matching models based on 3D CNNs have achieved excellent performance. However, huge computational burden and memory footprint of deep 3D convolution limit their deployment on edge devices and real-time scenes. Furthermore, given that disparity maps exhibit continuous disparity variations in content regions but sharp disparity variants at edge areas, spatially shared weight mechanism inherent in convolution may struggle to handle both types of regions. In this paper, we propose a lightweight stereo matching model, called DBCANet, with Dual-Branch Cost Aggregation based on region segmentation to reduce the computational burden. Specifically, we divide the cost aggregation module into two branches: the edge branch is to aggregate edge disparity information, and the content branch is to aggregate the disparity information of the content region. 2D convolution reduces the computational burden and dual branch can mitigate the problem caused by convolutional spatially-shared weights to compensate for the accuracy loss. We extensively validate our model on Scene Flow, KITTI datasets, and our model can achieve the lowest multiply-accumulate operations (MACs) among speed-oriented stereo matching models, while its EPE is only 0.59 on Scene Flow dataset, which is ranked second. Our model can achieve a good balance between accuracy and model computation.