Multi-line structured light binocular vision stereo matching method via coarse-to-fine spatial geometric constraints

•Identifying the correspondence of multi-line structured light in binocular images.•Refining the matching light stripes via coarse-to-fine spatial geometric constraints.•The proposed method needs no coding scheme, and has high flexibility and speed.•The recognition accuracy of the proposed method in...

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Bibliographic Details
Published in:Optics and laser technology 2024-09, Vol.176, p.110950, Article 110950
Main Authors: Liu, Yuwei, Ou, Pan, Xu, Xinqi, Sun, Junhua
Format: Article
Language:English
Subjects:
Binocular stereo vision
Laser stripe recognition
Multi-line structured light
Stereo matching
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Summary:•Identifying the correspondence of multi-line structured light in binocular images.•Refining the matching light stripes via coarse-to-fine spatial geometric constraints.•The proposed method needs no coding scheme, and has high flexibility and speed.•The recognition accuracy of the proposed method in different scenarios reaches 99.4%. In the 3D reconstruction of binocular stereo vision with multi-line structured light, the structured light projections in the 2D image may become distorted, cracked, or disappear due to the modulation of the surface of the object being measured. Correctly distinguishing the light plane to which the laser stripe belongs from the image is a fundamental and challenging problem in structured light binocular vision. This has a direct impact on the effectiveness of reconstructing 3D information. In this paper, we solve this problem via coarse to refined spatial geometric constraints. Firstly, a pre-location method for laser stripe points based on the perspective projection relationship is proposed. The method coarsely identifies the relationship between the corresponding laser stripes in the binocular image and the light plane to which they belong. Then, the minimum distance constraint from the point to the light plane is employed to refine the correlation between the corresponding laser stripes in the binocular image. Finally, the proposed method achieves an average accuracy of 99.4 % when experimentally analyzing different measured objects. The method has been implemented on a GPU using CUDA, with an average processing time of 35 ms per frame.
ISSN:0030-3992
DOI:10.1016/j.optlastec.2024.110950