Iterative Semi-Global Matching for Robust Driver Assistance Systems

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.

[1]  Horst Bischof,et al.  A Duality Based Approach for Realtime TV-L1 Optical Flow , 2007, DAGM-Symposium.

[2]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Horst Bischof,et al.  Pushing the limits of stereo using variational stereo estimation , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[4]  Heiko Hirschmüller,et al.  Evaluation of Stereo Matching Costs on Images with Radiometric Differences , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Jan-Olof Eklundh,et al.  Computer Vision — ECCV '94 , 1994, Lecture Notes in Computer Science.

[6]  Ines Ernst,et al.  Mutual Information Based Semi-Global Stereo Matching on the GPU , 2008, ISVC.

[7]  Tamir Hazan,et al.  Continuous Markov Random Fields for Robust Stereo Estimation , 2012, ECCV.

[8]  Sergiu Nedevschi,et al.  SORT-SGM: Subpixel Optimized Real-Time Semiglobal Matching for Intelligent Vehicles , 2012, IEEE Transactions on Vehicular Technology.

[9]  Takeo Kanade,et al.  Stereo by Two-Level Dynamic Programming , 1985, IJCAI.

[10]  Henry S. Warren,et al.  Hacker's Delight , 2002 .

[11]  Luis Rueda,et al.  Advances in Image and Video Technology, Second Pacific Rim Symposium, PSIVT 2007, Santiago, Chile, December 17-19, 2007, Proceedings , 2007, PSIVT.

[12]  Allen R. Hanson,et al.  Computer Vision Systems , 1978 .

[13]  Reinhard Klette,et al.  Illumination Invariant Cost Functions in Semi-Global Matching , 2010, ACCV Workshops.

[14]  Stefan K. Gehrig,et al.  A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching , 2009, ICVS.

[15]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[16]  H. Hirschmüller.  Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information , 2005, CVPR.

[17]  Radim Sára,et al.  A Weak Structure Model for Regular Pattern Recognition Applied to Facade Images , 2010, ACCV.

[18]  Reinhard Koch,et al.  Computer Vision – ACCV 2010 Workshops , 2010, Lecture Notes in Computer Science.

[19]  Reinhard Klette,et al.  Evaluation of a New Coarse-to-Fine Strategy for Fast Semi-Global Stereo Matching , 2011, PSIVT.

[20]  Bernd Jähne,et al.  Outdoor stereo camera system for the generation of real-world benchmark data sets , 2012 .

[21]  Heiko Hirschm,et al.  Accurate and Efcient Stereo Processing by Semi-Global Matching and Mutual Information , 2005 .

[22]  Minglun Gong,et al.  Fast stereo matching using reliability-based dynamic programming and consistency constraints , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[23]  Andreas Geiger,et al.  Efficient Large-Scale Stereo Matching , 2010, ACCV.

[24]  Masatoshi Okutomi,et al.  An analysis of sub-pixel estimation error on area-based image matching , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).