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Performance Evaluation of 3D Local Feature Descriptors

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9004))

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Abstract

A number of 3D local feature descriptors have been proposed in literature. It is however, unclear which descriptors are more appropriate for a particular application. This paper compares nine popular local descriptors in the context of 3D shape retrieval, 3D object recognition, and 3D modeling. We first evaluate these descriptors on six popular datasets in terms of descriptiveness. We then test their robustness with respect to support radius, Gaussian noise, shot noise, varying mesh resolution, image boundary, and keypoint localization errors. Our extensive tests show that Tri-Spin-Images (TriSI) has the best overall performance across all datasets. Unique Shape Context (USC), Rotational Projection Statistics (RoPS), 3D Shape Context (3DSC), and Signature of Histograms of OrienTations (SHOT) also achieved overall acceptable results.

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Acknowledgement

This research was supported in part by the National Natural Science Foundation of China under Grant 61471371, and in part by the Australian Research Council under Grants DE120102960 and DP110102166.

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Authors and Affiliations

  1. College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China

    Yulan Guo, Min Lu, Jianwei Wan & Jun Zhang

  2. School of Computer Science and Software Engineering, The University of Western Australia, Crawley, Australia

    Yulan Guo, Mohammed Bennamoun & Ferdous Sohel

Authors
  1. Yulan Guo
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  2. Mohammed Bennamoun
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  3. Ferdous Sohel
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  4. Min Lu
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  5. Jianwei Wan
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  6. Jun Zhang
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Corresponding author

Correspondence to Yulan Guo .

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Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J., Zhang, J. (2015). Performance Evaluation of 3D Local Feature Descriptors. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-16808-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16807-4

  • Online ISBN: 978-3-319-16808-1

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