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Fast and automatic object pose estimation for range images on the GPU

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Abstract

We present a pose estimation method for rigid objects from single range images. Using 3D models of the objects, many pose hypotheses are compared in a data-parallel version of the downhill simplex algorithm with an image-based error function. The pose hypothesis with the lowest error value yields the pose estimation (location and orientation), which is refined using ICP. The algorithm is designed especially for implementation on the GPU. It is completely automatic, fast, robust to occlusion and cluttered scenes, and scales with the number of different object types. We apply the system to bin picking, and evaluate it on cluttered scenes. Comprehensive experiments on challenging synthetic and real-world data demonstrate the effectiveness of our method.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Inha University, Incheon, 402-751, Korea

    In Kyu Park

  2. Computer Graphics Lab., Swiss Federal Institute of Technology (ETH), 8092, Zurich, Switzerland

    Marcel Germann

  3. Computer Vision Lab., Swiss Federal Institute of Technology (ETH), 8092, Zurich, Switzerland

    Michael D. Breitenstein

  4. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA

    Hanspeter Pfister

Authors
  1. In Kyu Park
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  2. Marcel Germann
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  3. Michael D. Breitenstein
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  4. Hanspeter Pfister
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Correspondence to In Kyu Park.

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Park, I.K., Germann, M., Breitenstein, M.D. et al. Fast and automatic object pose estimation for range images on the GPU. Machine Vision and Applications 21, 749–766 (2010). https://doi.org/10.1007/s00138-009-0209-8

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  • DOI: https://doi.org/10.1007/s00138-009-0209-8

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