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DPN-LRF: A Local Reference Frame for Robustly Handling Density Differences and Partial Occlusions

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9474))

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Abstract

For the purpose of 3D keypoint matching, a Local Reference Frame (LRF), a local coordinate system of the keypoint, is one important information source for achieving repeatable feature descriptions and accurate pose estimations. We propose a robust LRF for two main point cloud disturbances: density differences and partial occlusions. To generate LRFs that are robust to such disturbances, we employ two strategies: normalizing the effects of point cloud density by approximating the surface area in the local region and using the dominant orientation of a normal vector around the keypoint. Experiments confirm that the proposed method has higher repeatability than state-of-the-art methods with respect to density differences and partial occlusions. It was also confirmed that the method enhances the reliability of keypoint matching.

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References

  1. Frome, A., Huber, D., Kolluri, R., Bülow, T., Malik, J.: Recognizing objects in range data using regional point descriptors. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3023, pp. 224–237. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  2. Guo, Y., Sohel, F.A., Bennamoun, M., Lu, M., Wan, J.: Rotational projection statistics for 3D local surface description and object recognition. Int. J. Comput. Vis. 105, 63–86 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  3. Mian, A.S., Bennamoun, M., Owens, R.A.: On the repeatability and quality of keypoints for local feature-based 3d object retrieval from cluttered scenes. Int. J. Comput. Vis. 89, 348–361 (2010)

    Article  Google Scholar 

  4. Tombari, F., Salti, S., di Stefano, L.: Unique signatures of histograms for local surface description. In: European Conference on Computer Vision, pp. 356–369 (2010)

    Google Scholar 

  5. Petrelli, A., di Stefano, L.: On the repeatability of the local reference frame for partial shape matching. In: IEEE International Conference on Computer Vision, pp. 2244–2251 (2011)

    Google Scholar 

  6. Zhong, Y.: Intrinsic shape signatures: a shape descriptor for 3d object recognition. In: Proceedings of the International Conference on Computer Vision Workshops, pp. 689–696 (2009)

    Google Scholar 

  7. dos Santos, T.R., Franz, A.M., Meinzer, H., Maier-Hein, L.: Robust multi-modal surface matching for intra-operative registration. In: IEEE International Symposium on Computer-Based@Medical Systems, pp. 1–6 (2011)

    Google Scholar 

  8. Stein, F., Medioni, G.G.: Structural indexing: Efficient 3-d object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14, 125–145 (1992)

    Article  Google Scholar 

  9. Chua, C.S., Jarvis, R.: Point signatures: a new representation for 3D object recognition. Int. J. Comput. Vis. 25, 63–85 (1997)

    Article  Google Scholar 

  10. Sun, Y., Abidi, M.A.: Surface matching by 3D point’s fingerprint. In: ICCV, pp. 263–269 (2001)

    Google Scholar 

  11. Novatnack, J., Nishino, K.: Scale-Dependent/Invariant local 3D shape descriptors for fully automatic registration of multiple sets of range images. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 440–453. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  12. Zaharescu, A., Boyer, E., Varansi, K., Horaud, R.: Surface feature detection and description with applications to mesh matching. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 373–380 (2009)

    Google Scholar 

  13. Katz, S., Tal, A., Basri, R.: Direct visibility of point sets. ACM Trans. Graph. 26, 24 (2007)

    Article  Google Scholar 

  14. Rusu, R.B., Cousins, S.: 3d is here: Point cloud library (PCL). In: IEEE International Conference on Robotics and Automation, ICRA, IEEE (2011)

    Google Scholar 

  15. Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: CVPR (2), pp. 506–513 (2004)

    Google Scholar 

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Acknowledgements

This work was partially supported by Grant-in-Aid for Scientific Research (C) 26420398.

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Authors and Affiliations

  1. Graduate School of Science and Technology, Chukyo University, 101-2, Yagoto-Honmachi, Showa-ku, Nagoya, Aichi, Japan

    Shuichi Akizuki & Manabu Hashimoto

Authors
  1. Shuichi Akizuki
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  2. Manabu Hashimoto
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Corresponding author

Correspondence to Shuichi Akizuki .

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. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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Akizuki, S., Hashimoto, M. (2015). DPN-LRF: A Local Reference Frame for Robustly Handling Density Differences and Partial Occlusions. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_78

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_78

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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