by Hermann, S and Klette, R
Abstract:
Semi-global matching (SGM) is a technique of choice for dense stereo estimation in current industrial driver-assistance systems due to its real-time processing capability and its convincing performance. In this paper we introduce iSGM as a new cost integration concept for semi-global matching. In iSGM, accumulated costs are iteratively evaluated and intermediate disparity results serve as input to generate semi-global distance maps. This novel data structure supports fast analysis of spatial disparity information and allows for reliable search space reduction in consecutive cost accumulation. As a consequence horizontal costs are stabilized which improves the robustness of the matching result. We demonstrate the superiority of this iterative integration concept against a standard configuration of semi-global matching and compare our results to current state-of-the-art methods on the KITTI Vision Benchmark Suite. © 2013 Springer-Verlag.
Reference:
Iterative semi-global matching for robust driver assistance systems (Hermann, S and Klette, R), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 7726 LNCS, 2013.
Bibtex Entry:
@inproceedings{hermann2013iterativesystems,
author = "Hermann, S and Klette, R",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "465--478",
title = "Iterative semi-global matching for robust driver assistance systems",
volume = "7726 LNCS",
year = "2013",
abstract = "Semi-global matching (SGM) is a technique of choice for dense stereo estimation in current industrial driver-assistance systems due to its real-time processing capability and its convincing performance. In this paper we introduce iSGM as a new cost integration concept for semi-global matching. In iSGM, accumulated costs are iteratively evaluated and intermediate disparity results serve as input to generate semi-global distance maps. This novel data structure supports fast analysis of spatial disparity information and allows for reliable search space reduction in consecutive cost accumulation. As a consequence horizontal costs are stabilized which improves the robustness of the matching result. We demonstrate the superiority of this iterative integration concept against a standard configuration of semi-global matching and compare our results to current state-of-the-art methods on the KITTI Vision Benchmark Suite. © 2013 Springer-Verlag.",
doi = "10.1007/978-3-642-37431-9_36",
isbn = "9783642374302",
issn = "0302-9743",
eissn = "1611-3349",
issue = "PART 3",
language = "eng",
}