Binocular Vision Stereo Matching Method and Device Based on Plane Constraint and Triangulation

Abstract

Embodiments of the invention provide a plane constraint and triangulation-based binocular vision stereo matching method and device. The method comprises the following steps of: obtaining a left imageand a right image acquired by two cameras and taking any image as a reference image; determining a support point of the reference image and calculating parallax; segmenting the reference image according to a preset manner; dividing the segmented areas into first-type segmented areas and / or second-type segmented areas; segmenting each first-type segmented area for the second time; determining parallaxes of non-support points in each sub-segmented area according to the support point and parallax of the sub-segmented area; carrying out triangulation on each second-type segmented area; determiningparallax search ranges of non-support points in each triangular area according to a peak and a parallax of the triangular area; and matching to-be-matched points corresponding to the ranges with thenon-support points pixel by pixel so as to determine parallaxes of the non-support points. According to the method and device, shielded areas and large-area texture-free areas can be matched more correctly, the parallax search ranges of small-area segmented areas are decreased and the matching efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a binocular vision stereo matching method and device based on plane constraints and triangulation. Background technique [0002] Binocular stereo vision mainly includes four steps: binocular camera calibration, acquisition of two-dimensional image pairs, image correction, and binocular vision stereo matching. Among them, binocular vision stereo matching includes: first, use a binocular camera or two parallel cameras to shoot the same physical scene at the same time to obtain left and right images; then find the projection point of the same target in the scene in the left and right images, which is called the corresponding point ;Finally, a disparity map is generated according to the disparity of the corresponding point, that is, the offset of the corresponding point in the u-axis direction of the pixel coordinate system. The actual distance between the target and the cam...

AI Technical Summary

Problems solved by technology

However, the above-mentioned stereo matching algorithms are all matched pixel by pixel. Therefore, for the lack of pixel information in occluded areas and the ambiguity in matching large areas without texture, the above-mentioned stereo matching algorithms cannot well solve the problem of occluded areas or large areas. The matching of areas without texture will result in higher matching error rate and lower matching efficiency

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