Multi-Dimensional Cooperative Network for Stereo Matching

The dimensions of cost volumes and corresponding aggregation networks play a critical role in balancing the speed and accuracy for stereo matching. Current most 2D stereo networks based on 3D cost volumes are speedy but poor in precision, while 3D stereo networks with 4D volumes are slow but precise. To tackle this problem, we propose a novel stereo framework for combining 2D and 3D networks. The disparity range generated by the 2D network is used to guide the 3D network and reduce the computational complexity. Meanwhile, the 3D network can refine the rough disparity from coarse to fine. Through the cooperation of 2D and 3D networks, our method can achieve accurate stereo results at a fast speed. We evaluate our method on three popular public benchmark datasets. Experimental results from the KITTI official website show that, our method can achieve similar accuracy with other 3D stereo networks (PSMNet, GCNet, GwcNet, etc.) at a significantly faster speed. The ablation studies further demonstrate the facticity and reasonability of our propose method.