MSDC-Net: Multi-Scale Dense and Contextual Networks for Stereo Matching

Disparity prediction from stereo images is essential to computer vision applications such as autonomous driving, 3D model reconstruction, and object detection. To more accurately predict disparity map, a novel deep learning architecture (called MSDC-Net) for detecting the disparity map from a rectified pair of stereo images is proposed. Our MSDC-Net contains two modules: the multi-scale fusion 2D convolution module and the multi-scale residual 3D convolution module. The multi-scale fusion 2D convolution module exploits the potential multi-scale features, which extracts and fuses the different scale features by Dense-Net. The multi-scale residual 3D convolution module learns the different scale geometry context from the cost volume which aggregated by the multi-scale fusion 2D convolution module. Experimental results on Scene Flow and KITTI datasets demonstrate that our MSDC-Net significantly outperforms other approaches in the non-occluded region.