Improved binocular stereo matching method based on PSMNet

Abstract

The invention relates to an improved binocular stereo matching method based on PSMNet, and the method comprises the steps: obtaining a binocular image, and constructing a backbone network based on PSMNet; wherein the network comprises a deep convolutional network used for extracting left and right feature maps of a binocular image; a pyramid pooling structure used for extracting multi-scale targetfeatures of the left and right feature maps; a matching cost volume used for performing cost aggregation on the multi-scale target features to obtain a 3D feature module; a 3D convolution structure used for carrying out subsequent cost calculation on the 3D feature module; giving different weights to different feature points by introducing a channel attention mechanism to improve the structure ofa matching cost volume; designing a network structure based on an encoding process and a decoding process to improve a 3D convolution structure to obtain an improved PSMNet-based backbone network; and carrying out stereo matching on the binocular image. The stereo matching method can enable the network structure to obtain faster training time and higher parallax precision, and has better practicability.

Description

technical field [0001] The invention relates to the technical field of computer vision applications, in particular to an improved binocular stereo matching method based on PSMNet. Background technique [0002] Stereo vision is an important topic in the field of computer vision. Its purpose is to reconstruct the three-dimensional geometric information of the scene. We can use the binocular stereo camera to obtain the left and right views of the current scene, and then use the stereo matching algorithm to calculate the depth of the current scene. information. Stereo matching is a key part of obtaining target depth information in stereo vision. Its goal is to match corresponding pixel points in two or more viewpoints, calculate parallax and depth, and obtain 3D information of the scene. [0003] A complete stereo matching algorithm usually includes four steps: matching cost calculation, cost aggregation, disparity calculation, and disparity refinement. Traditional stereo matc...

AI Technical Summary

Problems solved by technology

However, this type of CNN-based stereo matching algorithm often only uses the twin network for matching cost calculation, and the calculated disparity value still needs to be post-processed to improve its accuracy.

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