Fast iterative computation method for semi-dense stereo matching

A stereo matching and iterative calculation technology, applied in the field of computer vision, can solve the problems of high processing frame rate, inability to obtain high processing efficiency, and inability to adapt to industry requirements, and achieve the effect of accurate disparity estimation and improved image matching accuracy.

Active Publication Date: 2018-02-23
CHENGDU TOPPLUSVISION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to its inherent computational limitations, dense matching cannot achieve higher processing frame rates (eg, VGA image efficiency of 30fps or higher under equivalent computing conditions)
[0003] Therefore, the above two stereo schemes in the traditional technology have the following d

Method used

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  • Fast iterative computation method for semi-dense stereo matching
  • Fast iterative computation method for semi-dense stereo matching
  • Fast iterative computation method for semi-dense stereo matching

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Embodiment

[0051] Such as figure 1 As shown, the fast iterative calculation method for semi-dense stereo matching in this embodiment includes the following implementation steps:

[0052] S1: Use the binocular camera to obtain the left image and the right image;

[0053] In this embodiment, the image acquired by the left camera of the binocular camera is called the left image, and the image acquired by the right camera of the binocular camera is called the right image.

[0054] S2: Extract the feature points of the left image and the right image respectively, and construct a feature descriptor;

[0055]This embodiment utilizes the SIFT algorithm (scale-invariant feature transformation algorithm) to extract feature points from the left image and the right image respectively:

[0056] That is, first use the Gaussian filter to perform multiple consecutive filters on the left or right image to establish the first scale group; then reduce the left or right image to half of its original size ...

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Abstract

The invention relates to the field of computer vision technologies, and discloses a fast iterative computation method for semi-dense stereo matching, which accelerates the matching speed while satisfying the requirement of acquiring high matching precision. The fast iterative computation method can be summarized in the steps of: extracting feature points of a left image and a right image separately, and constructing feature descriptors; then, completing feature point matching of the left and right images according to the feature descriptors and an epipolar constraint, wherein the successfullymatched feature points are called supporting points; further, constructing a Delaunay triangle according to the supporting points in the left image and estimating prior parallaxes d of all pixel points in the left image; and calculating cost errors C corresponding to the prior parallaxes d, and acquiring the minimum cost error C<min> of all the supporting points during current iteration;iteratively updating a supporting point set and the minimum cost error until an iteration termination condition is satisfied; and finally, calculating a matching point in the right image of each pixelpoint in the left image according to the parallax of the pixel point.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a fast iterative calculation method for semi-dense stereo matching. Background technique [0002] Image stereo matching is an important branch of computer vision, photogrammetry, and computer graphics, and it is of great value in many applications. Image matching can be divided into sparse matching and dense matching. Sparse matching generally extracts the feature points with strong texture on the image, and then calculates the matching cost through the feature descriptor to obtain the optimal matching. Due to the sparsity of feature points, sparse matching cannot provide a sufficient number of feature points and 3D points in many applications, so only limited 3D world information can be obtained. Dense matching is to match each pixel of the image, so dense 3D world information can be obtained. Algorithms for dense matching can be divided into global methods and local ...

Claims

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Application Information

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IPC IPC(8): G06T7/33
CPCG06T2207/10012G06T7/33
Inventor 周剑唐荣富龙学军徐一丹
Owner CHENGDU TOPPLUSVISION TECH CO LTD
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