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An Efficient Algorithm for Feature-Based 3D Point Cloud Correspondence Search

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Advances in Visual Computing (ISVC 2016)

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Abstract

Searching correspondences between 3D point Clouds is computationally expensive for two reasons: the complexity of geometric-based feature extraction operations and the large search space. To tackle this challenging problem, we propose a novel and efficient 3D point cloud matching algorithm. Our algorithm is inspired by PatchMatch [1], which is designed for correspondence search between 2D images. However, PatchMatch relies on the natural scanline order of 2D images to propagate good solutions across the images, whereas such an order does not exist for 3D point clouds. Hence, unlike PatchMatch which conducts search at different pixels sequentially under the scanline order, our algorithm searches the best correspondences for different 3D points in parallel using a variant of the Artificial Bee Colony (ABC) [2] algorithm and propagates good solutions found at one point to its k-nearest neighbors. In addition, noticed that correspondences found using geometric-based features extracted at individual points alone can be prone to noise, we add a novel smooth term to the objective function. Experiments on multiple datasets show that the new smooth term can effectively suppress matching noises and the ABC-based parallel search can significantly reduce the computational time compared to brute-force search.

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Authors and Affiliations

  1. Department of Computer Science, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, A1B 3X5, Canada

    Zili Yi, Yang Li & Minglun Gong

Authors
  1. Zili Yi
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  2. Yang Li
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  3. Minglun Gong
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Corresponding author

Correspondence to Zili Yi .

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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Yi, Z., Li, Y., Gong, M. (2016). An Efficient Algorithm for Feature-Based 3D Point Cloud Correspondence Search. 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_44

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_44

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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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