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Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

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Abstract

Optical flow ground truth generated by computer graphics has many advantages. For example, we can systematically vary scene parameters to understand algorithm sensitivities. But is synthetic ground truth realistic enough? Appropriate material models have been established as one of the major challenges for the creation of synthetic datasets: previous research has shown that highly sophisticated reflectance field acquisition methods yield results, which various optical flow methods cannot distinguish from real scenes. However, such methods are costly both in acquisition and rendering time and thus infeasible for large datasets. In this paper we find the simplest reflectance models (RM) for different groups of materials which still provide sufficient accuracy for optical flow performance analysis. It turns out that a spatially varying Phong RM is sufficient for simple materials. Normal estimation combined with Anisotropic RM can handle even very complex materials.

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Authors and Affiliations

  1. Heidelberg Collaboratory for Image Processing, University of Heidelberg, Heidelberg, Germany

    Burkhard Güssefeld, Katrin Honauer & Daniel Kondermann

Authors
  1. Burkhard Güssefeld
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  2. Katrin Honauer
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  3. Daniel Kondermann
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Corresponding author

Correspondence to Burkhard Güssefeld .

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. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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Güssefeld, B., Honauer, K., Kondermann, D. (2016). Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

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