Abstract
In this paper, we propose a variant of Semi-Global Matching, hSGM which is a hierarchical pyramid based dense stereo matching algorithm. Our method aggregates the matching costs from the coarse to fine scale in multiple directions to determine the optimal disparity for each pixel. It has several advantages over the original SGM: a low space complexity and efficient implementation on GPU. We show several experimental results to demonstrate our method is efficient and obtains a good quality of disparity maps.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hirschmüller, H.: Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 807–814 (2005)
Guerra-Filho, G.: An Optimal Time-Space Algorithm for Dense Stereo Matching. Journal of Real-Time Image Processing, 1–18 (2010)
Ernst, I., Hirschmüller, H.: Mutual Information Based Semi-Global Stereo Matching on the GPU. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 228–239. Springer, Heidelberg (2008)
Gibson, J., Marques, O.: Stereo Depth with a Unified Architecture GPU. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (2008)
Humenberger, M., Engelke, T., Kubinger, W.: A Census-Based Stereo Vision Algorithm Using Modified Semi-Global Matching and Plane Fitting to Improve Matching Quality. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 6th Workshop on Embedded Computer Vision (2010)
Gehrig, S.K., Rabe, C.: Real-Time Semi-Global Matching on the CPU. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 6th Workshop on Embedded Computer Vision (2010)
Humenberger, M., Zinner, C., Kubinger, W.: Performance Evaluation of a Census-Based Stereo Matching Algorithm on Embedded and Multi-Core Hardware. In: Proceedings of the 6th Int. Symposium on Image and Signal Processing and Analysis (2009)
Kolmogorov, V., Zabih, R.: Computing Visual Correspondence with Occlusions using Graph Cuts. In: Int. Conference on Computer Vision, vol. 2, pp. 508–515 (2001)
Sum, J., Li, Y., Kang, S., Shum, H.-Y.: Symmetric Stereo Matching for Occlusion Handling. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 399–406 (2005)
Hirschmüller, H.: Stereo Vision in Structured Environments by Consistent Semi-Global Matching. In: IEEE Conference on Computer Vision and Pattern Recognition (2006)
Kim, J., Kolmogorov, V., Zabih, R.: Visual Correspondence Using Energy Minimization and Mutual Information. In: IEEE International Conference on Computer Vision (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Won, K.H., Jung, S.K. (2011). hSGM: Hierarchical Pyramid Based Stereo Matching Algorithm. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_62
Download citation
DOI: https://doi.org/10.1007/978-3-642-23687-7_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23686-0
Online ISBN: 978-3-642-23687-7
eBook Packages: Computer ScienceComputer Science (R0)