EVALUATION OF PENALTY FUNCTIONS FOR SEMI-GLOBAL MATCHING COST AGGREGATION

9 citations as recorded by crossref.

1. Dense Matching with Low Computational Complexity for Disparity Estimation in the Radargrammetric Approach of SAR Intensity Images H. Jannati et al. https://doi.org/10.3390/rs17152693
2. Feature Engineering and Deep Learning for Stereo Matching Under Adverse Driving Conditions V. Dinh et al. https://doi.org/10.1109/TITS.2021.3073557
3. Real-Time Semantic Segmentation-Based Stereo Reconstruction V. Miclea & S. Nedevschi https://doi.org/10.1109/TITS.2019.2913883
4. Radargrammetric DSM Generation by Semi-Global Matching and Evaluation of Penalty Functions J. Wang et al. https://doi.org/10.3390/rs14081778
5. Adaptive smooth constrained stereo matching and disparity refinement for satellite DSM generation C. Xu et al. https://doi.org/10.1080/15481603.2026.2745213
6. Active Stereo in the Wild through Virtual Pattern Projection L. Bartolomei et al. https://doi.org/10.1007/s11263-025-02511-6
7. A vision processor with a unified interest point detection and matching hardware for accelerating stereo matching algorithm J. Park et al. https://doi.org/10.1109/TCSVT.2015.2450391
8. Dense Stereo Matching With Edge-Constrained Penalty Tuning T. Chuang et al. https://doi.org/10.1109/LGRS.2018.2805916
9. Low Complexity, Hardware-Efficient Neighbor-Guided SGM Optical Flow for Low-Power Mobile Vision Applications Z. Li et al. https://doi.org/10.1109/TCSVT.2018.2854284