To address the challenges of large-scale and complex network structures in deep learning-based stereo matching,this work introduces a compact yet highly accurate network.The feature extraction mod-ule simplifies by removing complex,redundant residual layers and incorporating an Atrous Spatial Pyramid Pooling(ASPP)module to broaden the field of view and enhance contextual information extraction.For cost calculation,three-dimensional(3D)convolutional layers refine stereo matching accuracy through cost aggregation.In addition,a bilateral grid module is integrated into the cost aggregation process,achieving precise disparity maps with reduced resolution demands.Tested on widely-used datasets like KITTI 2015 and Scene Flow,our network demonstrates a significant reduction in parameters by approximately 38%compared to leading networks like Pyramid Stereo Matching Network(PSM-Net),without compromis-ing on experimental accuracy.Notably,it achieves an end-point error(EPE)of 0.86 on the Scene Flow dataset,outperforming many top-performing networks.Thus,our network effectively balances speed and accuracy in stereo matching.
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