A stereo matching method (HybridNet) based on hybrid 2D convolution and pseudo 3D convolution, the method relating to the field of computer vision. The method comprises the following steps: extracting an image feature on the basis of a preset parameter, so as to obtain a feature map (S101); generating a cost volume on the basis of the feature map (S102); obtaining, by means of a PSMNet structure, a cost volume that has been subjected to cost aggregation, and finally, obtaining an initial disparity map by means of disparity regression; obtaining a residual cost volume by means of the initial disparity map, performing residual aggregation and then obtaining a disparity residual, so as to optimize the initial disparity map, wherein in the PSMNet structure and during residual aggregation, 3D convolution is replaced with a combination of hybrid 2D convolution and pseudo 3D convolution (S103); and performing depth map optimization on the disparity map by using a CSPNet method (S105). The function of 3D convolution is approximately implemented in combination with 2D convolution, and such a data switching operation does not include a learnable parameter and does not generate a calculation amount. By means of cost aggregation of hybrid 2D convolution and pseudo 3D convolution, the calculation amount of an existing model can be greatly reduced with a tiny loss in precision.
STEREO MATCHING METHOD BASED ON HYBRID 2D CONVOLUTION AND PSEUDO 3D CONVOLUTION