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Stereo-Matching in the Context of Vision-Augmented Vehicles

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

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Abstract

Stereo matching accuracy is determined by comparing results with ground truth. However, the kind of detail remains unspecified in regions where a stereo matcher is more accurate. By identifying feature points we are identifying regions where the data cost used can easily represent features. We suggest to use feature matchers for identifying sparse matches of high confidence, and to use those for guiding a belief-propagation mechanism.

Extensive experiments, also including a semi-global stereo matcher, illustrate achieved performance. We also test on data just recently made available for a developing country, which comes with particular challenges not seen before. Since KITTI ground truth is sparse, for most of identified feature points ground truth is actually missing. By using our novel stereo matching method (called FlinBPM) we derive our own ground truth and compare it with results obtained by other matching approaches including our novel stereo matching method (called WlinBPM).

Based on this we were able to identify circumstances in which a census transform fails to define an appropriate data cost measure. There is not a single all-time winner in the set of considered stereo matchers, but there are specific benefits when applying one of the discussed stereo matching strategies. This might point towards a need of adaptive solutions for vision-augmented vehicles.

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Notes

  1. 1.

    On \(28^{th}\) August, 2015 our stereo matcher WlinBPM ranked \(46^{th}\) at [5] compared to linBPM and iSGM which ranked \(57^{th}\) and \(32^{nd}\), respectively.

  2. 2.

    Authors acknowledge the support by Mick Jays, Ian Armstrong and Jeff Echano for allowing overnight access to multiple computers for this sequence.

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Author information

Authors and Affiliations

  1. School of Business and Information Technology, Petone Campus, Wellington Institute of Technology, Wellington, New Zealand

    Waqar Khan

  2. EEE Department, School of Engineering, Auckland University of Technology, Auckland, New Zealand

    Reinhard Klette

Authors
  1. Waqar Khan
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  2. Reinhard Klette
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Corresponding author

Correspondence to Waqar Khan .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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Khan, W., Klette, R. (2015). Stereo-Matching in the Context of Vision-Augmented Vehicles. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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