Abstract
Semi-global matching (SGM) has been widely used in binocular vision. In spite of its good efficiency, SGM still has difficulties in dealing with low-texture regions. In this paper, an SGM algorithm based on multi-scale information fusion (MSIF), named SGM-MSIF, is proposed by combining multi-path cost aggregation and cross-scale cost aggregation (CSCA). Firstly, the stereo pairs at different scales are obtained by Gaussian pyramid down-sampling. The initial matching cost volumes at different scales are computed by combining census transform and color information. Then, the multi-path cost aggregation in SGM is introduced into the cost aggregation at each scale and the aggregated cost volumes are fused by CSCA. Thirdly, the disparity map is optimized by internal left-right consistency check and median filter. Finally, experiments are conducted on Middlebury datasets to evaluate the proposed algorithm. Experimental results show that the average error matching rate (EMR) of the proposed SGM-MSIF algorithm reduced by 1.96% compared with SGM. Compared with classical cross-scale stereo matching algorithm, the average EMR of SGM-MSIF algorithm reduced by 0.92%, while the processing efficiency increased by 58.7%. In terms of overall performance, the proposed algorithm outperforms the classic SGM and CSCA algorithms. It can achieve high matching accuracy and high processing efficiency for binocular vision applications, especially for those with low-texture regions.
Funder
National Natural Science Foundation of China
National Key R&D Program of China
Key R&D Program of Hebei Province
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference33 articles.
1. Measurement of Large-Sized-Pipe Diameter Based on Stereo Vision;Liu;Appl. Sci.,2022
2. Do, P.N.B., and Nguyen, Q.C. (2019, January 25–27). A review of stereo-photogrammetry method for 3-D reconstruction in computer vision. Proceedings of the 19th International Symposium on Communications and Information Technologies (ISCIT), Ho Chi Minh City, Vietnam.
3. Huynh, T.H., and Yoo, M. (2020). A Taillight Matching and Pairing Algorithm for Stereo-Vision-Based Nighttime Vehicle-to-Vehicle Positioning. Appl. Sci., 10.
4. Research on Vehicle Adaptive Real-time Positioning Based on Binocular Vision;Zhou;IEEE Intell. Transp. Syst. Mag.,2021
5. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms;Scharstein;Int. J. Comput. Vis.,2002
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