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International Symposium on Visual Computing

ISVC 2013: Advances in Visual Computing pp 333–342Cite as

Rapid Disparity Prediction for Dynamic Scenes

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8033))

Abstract

Real-time 3D sensing plays a critical role in robotic navigation, video surveillance and human-computer interaction, etc. When computing 3D structures of dynamic scenes from stereo sequences, spatiotemporal stereo and scene flow methods can produce temporally coherent disparity. However, most existing methods do not utilize the previous disparity map sufficiently to compute the next disparity map, and the searching space of correspondences limits the speed of disparity computation for each image pair. This paper proposes an effective scheme to predict disparity maps from stereo sequences. In particular, we apply a robust 3D registration algorithm based on the angular-invariant feature to estimate the ego-motion of the stereo rig between consecutive frames, and present the transformation between consecutive disparity maps. The scheme can produce a sequence of temporally coherent disparity maps rapidly. We apply the new scheme to real outdoor scenes, and thorough empirical studies indicate the effectiveness of the new scheme for practical applications.

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Author information

Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

    Jun Jiang & Jun Cheng

  2. The Chinese University of Hong Kong, China

    Jun Jiang & Jun Cheng

  3. Shenzhen Institute of Information Technology, China

    Baowen Chen

  4. Guangdong Provincial Key Laboratory of Robotics and Intelligent System, China

    Jun Cheng

  5. The Shenzhen Key Laboratory of Computer Vision and Pattern Recognition, China

    Jun Jiang

Authors
  1. Jun Jiang
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  2. Jun Cheng
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  3. Baowen Chen
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Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

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

    Richard Boyle

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

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Arizona State University, Tempe, AZ, USA

    Baoxin Li

  6. Mitsubishi Electric Research Laboratories, Cambridge, MA, USA

    Fatih Porikli

  7. University of California, Riverside, CA, USA

    Victor Zordan

  8. AT&T Research Labs, Florham Park, NJ, USA

    James Klosowski

  9. ZIRST, Saint-Ismier Cedex, France

    Sabine Coquillart

  10. Qualcomm Research, San Diego, CA, USA

    Xun Luo

  11. Oxford e-Research Centre, University of Oxford, Oxford, UK

    Min Chen

  12. IBM, Hawthorne, NY, USA

    David Gotz

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© 2013 Springer-Verlag Berlin Heidelberg

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Cite this paper

Jiang, J., Cheng, J., Chen, B. (2013). Rapid Disparity Prediction for Dynamic Scenes. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_33

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  • DOI: https://doi.org/10.1007/978-3-642-41914-0_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41913-3

  • Online ISBN: 978-3-642-41914-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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