Binocular stereoscopic vision-based stereo matching method

A binocular stereo vision and stereo matching technology, which is applied in the field of computer vision, can solve the problems of increasing the difficulty of stereo matching, discontinuous parallax, and prone to wrong matching, so as to solve the problem of parallax calculation in areas where the parallax is discontinuous and cannot be matched. , the effect of mitigating weak and repetitive textures, improving quality

Inactive Publication Date: 2017-01-18
SOUTHEAST UNIV
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Problems solved by technology

They are caused by objective factors such as cameras and other equipment and the environment, and have little to do with the algorithm itself, but they must be disturbed by imbalances such as brightness, hue, and saturation in the processing process
[0005] (2) Parallax discontinuity problem: the disparity values ​​in the contour area where different objects intersect are discontinuous, and the texture information is particularly rich in the discontinuous disparity area, which undoubtedly increases the difficulty of stereo matching, so when performing disparity estimation,

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  • Binocular stereoscopic vision-based stereo matching method
  • Binocular stereoscopic vision-based stereo matching method
  • Binocular stereoscopic vision-based stereo matching method

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[0041] In the following, the present invention will be further explained through examples in combination with the drawings and the stereo matching process of Gaussian pyramid transform cross-scale cost aggregation:

[0042] 1. Calculate the matching cost

[0043] The matching cost is used to measure the similarity between two or more different images of the same scene taken from different perspectives under different parallaxes. The matching cost is calculated using f:R W×H×3 ×R W×H×3 →R W×H×L Represents, where W and H represent the width and height of the image resolution, 3 represents the pixel RGB channel, and L represents the maximum parallax d max . Two different stereoscopic image pairs I and I'of the same scene shot from different perspectives use equation (1) to express the matching cost volume from 1 to L in disparity.

[0044] C=f(I,I′) (1)

[0045] For x i ,y i The pixel point at the coordinate i=f(x i ,y i ), the matching cost under the disparity l is the scalar C(i,l...

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Abstract

The invention relates to a binocular stereoscopic vision-based stereo matching method. The method includes the following six stages: Gaussian pyramid construction; cost calculation matching and cost aggregation; cost fusion matching; disparity computation; disparity map repair and void filling; and disparity refinement. Laplacian pyramid transformation is additionally adopted in the cost aggregation stage. An edge-protection-based interpolation algorithm is used in the disparity map repair and hole filling stage. A weighting and bilateral filtering combination-based disparity refinement method is additionally adopted in the disparity refinement stage, so that a high-accuracy disparity map can be obtained. The calculation amount of the method of the invention is moderate; matching results at different scales are fused, improvement is made in the cost aggregation stage and the disparity refinement stage, and therefore, a better disparity map can be obtained; and the method has certain robustness to illumination, external noises and the like.

Description

technical field [0001] The invention relates to a stereo matching method based on binocular stereo vision, which is a method for calculating depth through stereo matching of image pairs based on binocular stereo vision, and belongs to the field of computer vision. Stereo matching is to estimate the scene by finding the matching pixels between two or more different images of the same scene taken from different perspectives, and then converting the 2D displacement difference (also called parallax disparity) between the matching pixels into 3D depth. A 3D model of . Stereo matching is widely used in virtual reality, robot navigation, 3D scene rendering and reconstruction, large-scale mechanical attitude perception and other fields. Background technique [0002] Professor D.Marr from the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology (MIT) proposed a visual computing theory that was successfully applied to binocular matching. Professor D.Marr ob...

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Application Information

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IPC IPC(8): G06T7/35
CPCG06T2207/10012G06T2207/20228
Inventor 姚莉刘祖奎王秉凤
Owner SOUTHEAST UNIV
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