Comparison of Stereo Matching Algorithms for the Development of Disparity Map
Keywords:Stereo matching, Cost function, Correspondence problem, Disparity map, Depth estimation
Stereo Matching is one of the classical problems in computer vision for the extraction of 3D information but still controversial for accuracy and processing costs. The use of matching techniques and cost functions is crucial in the development of the disparity map. This paper presents a comparative study of six different stereo matching algorithms including Block Matching (BM), Block Matching with Dynamic Programming (BMDP), Belief Propagation (BP), Gradient Feature Matching (GF), Histogram of Oriented Gradient (HOG) and Fixed-Window Aggregated Cost (FWAC). In addition, three cost functions, namely, Mean Squared Error (MSE), Sum of Absolute Differences (SAD), and Normalized Cross-Correlation (NCC) were utilized and compared. The stereo images used in this study were obtained from the Middlebury Stereo Datasets provided with perfect and imperfect calibrations. It was observed that the selection of matching function is quite important and also depends on the image properties. Results showed that the BP algorithm in most cases provided better results achieving accuracies over 95%. Accordingly, BP algorithm is highly recommended based on rapidity and performance and for applications with the need of detection of small details, HOG is advised.
Aboali, M., Abd Manap, N., & Yusof, Z. M. (2017). Performance analysis between basic block matching and dynamic programming of stereo matching algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-13), 7-16.
Chai, Y., & Cao, X. (2018, October). Stereo matching algorithm based on joint matching cost and adaptive window. In 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (pp. 442-446). IEEE. https://doi.org/10.1109/IAEAC.2018.8577495.
Çiğla, C., & Alatan, A. A. (2011, November). Efficient edge-preserving stereo matching. In 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) (pp. 696-699). IEEE. https://doi.org/10.1109/ICCVW.2011.6130315.
Dalal, N., & Triggs, B. (2005, June). Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) (Vol. 1, pp. 886-893). Ieee.
Egnal, G. (2000). Mutual information as a stereo correspondence measure. Technical Reports (CIS). University of Pennsylvania.
Felzenszwalb, P. F., & Huttenlocher, D. P. (2006). Efficient belief propagation for early vision. International journal of computer vision, 70, 41-54. https://doi.org/10.1007/s11263-006-7899-4.
Geiger, A., Roser, M., & Urtasun, R. (2010, November). Efficient Large-Scale Stereo Matching. In ACCV (1) (pp. 25-38). https://doi.org/10.1007/978-3-642-19315-6_3.
Gong, M., Yang, R., Wang, L., & Gong, M. (2007). A performance study on different cost aggregation approaches used in real-time stereo matching. International Journal of Computer Vision, 75, 283-296. https://doi.org/10.1007/s11263-006-0032-x.
Heise, P., Klose, S., Jensen, B., & Knoll, A. (2013). Pm-huber: Patchmatch with huber regularization for stereo matching. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2360-2367). https://doi.org/10.1109/ICCV.2013.293.
Heo, Y. S., Lee, K. M., & Lee, S. U. (2008, June). Illumination and camera invariant stereo matching. In 2008 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE.
Hirschmuller, H., & Scharstein, D. (2007, June). Evaluation of cost functions for stereo matching. In 2007 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE. https://doi.org/10.1109/CVPR.2007.383248.
Huang, Z., Gu, J., Li, J., & Yu, X. (2021). A stereo matching algorithm based on the improved PSMNet. Plos one, 16(8), e0251657. https://doi.org/10.1371/journal.pone.0251657.
Kim, J. (2003, October). Visual correspondence using energy minimization and mutual information. In Proceedings Ninth IEEE International Conference on Computer Vision (pp. 1033-1040). IEEE.
Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE transactions on pattern analysis and machine intelligence, 27(10), 1615-1630. https://doi.org/10.1109/TPAMI.2005.188.
Moravec, H. (1977). Towards Automatic Visual Obstacle Avoidance, 5th IJCAI.
Mozerov, M. G., & Van De Weijer, J. (2015). Accurate stereo matching by two-step energy minimization. IEEE Transactions on Image Processing, 24(3), 1153-1163. https://doi.org/10.1109/TIP.2015.2395820.
Pham, C. C., & Jeon, J. W. (2012). Domain transformation-based efficient cost aggregation for local stereo matching. IEEE Transactions on Circuits and Systems for Video Technology, 23(7), 1119-1130. https://doi.org/10.1109/TCSVT.2012.2223794.
Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., & Westling, P. (2014). High-resolution stereo datasets with subpixel-accurate ground truth. In Pattern Recognition: 36th German Conference, GCPR 2014, Münster, Germany, September 2-5, 2014, Proceedings 36 (pp. 31-42). Springer International Publishing. https://doi.org/10.1007/978-3-319-11752-2_3.
Zhu, S., & Yan, L. (2017). Local stereo matching algorithm with efficient matching cost and adaptive guided image filter. The Visual Computer, 33, 1087-1102. https://doi.org/10.1007/s00371-016-1264-6.
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Copyright (c) 2023 Hamid Fsian, Vahid Mohammadi, Pierre Gouton , Saeid Minaei
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