Parallax image generation method and system based on binocular stereo vision matching

Abstract

The present invention provides a parallax image generation method and system based on binocular stereo vision matching, wherein the parallax image generation method includes: acquiring a pair of speckle images; The image of the speckle captured by the camera after the binocular positioning; input the pair of speckle images into the preset deep learning neural network to generate a corresponding parallax image; wherein, the deep learning neural network uses multiple pairs of speckle image training. The present invention uses a pair of speckle images as input, and directly outputs the disparity map through the binocular vision stereo matching network (deep learning neural network), thereby realizing the end-to-end calculation of the disparity map and the end-to-end network design framework, eliminating the The post-processing steps of stereo matching, such as sub-pixel enhancement, filter noise reduction, refinement and other operations, greatly reduce the computational overhead.

Description

technical field [0001] The invention belongs to the field of image processing, and more specifically relates to a parallax image generation method, system, electronic equipment and readable medium based on binocular stereo vision matching. Background technique [0002] The rapid development of depth sensors has brought new changes to the field of computer vision, and depth estimation information has been fully applied to solutions to problems related to autonomous driving, 3D reconstruction, behavior recognition, and liveness detection. At present, mainstream depth sensors mainly include four types of technologies: time of flight (ToF), lidar, structured light and binocular stereo vision. [0003] The technical steps of binocular stereo vision include: camera calibration to obtain camera parameters; epipolar line correction to make the epipolar lines of the left and right cameras horizontal; image pixel matching after correction to obtain disparity map; depth information obt...

AI Technical Summary

Problems solved by technology

Therefore, the current binocular stereo matching method has the following shortcomings: Although the CNN-based matching method improves the matching speed and accuracy, it does not solve the essential problems of binocular stereo matching. area, it is impossible to accurately find matching points, and post-processing steps for stereo matching are required, such as sub-pixel enhancement, filter noise reduction, refinement, etc., the process is cumbersome

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