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 module 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 compromising 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.