Research on semiglobal stereo matching algorithm based on multicost

Fengyun Huang; Rui Fang; Jinli Xu

doi:10.1117/12.3035439

16 August 2024 Research on semiglobal stereo matching algorithm based on multicost

Fengyun Huang, Rui Fang, Jinli Xu

Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 1323024 (2024) https://doi.org/10.1117/12.3035439
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China

Abstract

Semi-global stereo matching (SGM) algorithm is widely adopted in stereo matching due to its optimal trade-off between accuracy and efficiency. However, SGM exhibits limitations in accurately matching weak texture regions and entails high computational complexity. The present paper proposes a novel enhanced SGM algorithm by integrating the CT cost and BT cost, aiming to address this issue. Initially, the anti-interference capability of Census transform is improved by setting a standard deviation threshold. The fused weights of the Census cost and window-based BT cost compensate for insufficient image information. Subsequently, an 8-channel dynamic programming algorithm is utilized to aggregate costs followed by a winner-take-all approach to compute disparity values. Furthermore, a weighted least squares filter optimizes the disparity map. Finally, the proposed algorithm's anti-occlusion performance and matching accuracy are evaluated using the Middlebury dataset. Experimental results demonstrate that our proposed cost calculation method outperforms CT cost and BT cost in terms of anti-interference performance significantly when compared with both SGM algorithm and AD-Census algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fengyun Huang, Rui Fang, and Jinli Xu "Research on semiglobal stereo matching algorithm based on multicost", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 1323024 (16 August 2024); https://doi.org/10.1117/12.3035439

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KEYWORDS

Windows

Tunable filters

Image filtering

Digital filtering

Mathematical optimization

Computer programming

Reconstruction algorithms

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