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International Symposium on Visual Computing

ISVC 2016: Advances in Visual Computing pp 410–420Cite as

Performance Evaluation of 3D Keypoints and Descriptors

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

Abstract

This paper presents a comprehensive evaluation of the performance of common 3D keypoint detectors and descriptors currently available in the Point Cloud Library (PCL) to recover the transformation of 300 real objects. Current research on keypoints detectors and descriptors considers their performance individually in terms of their repeatability or descriptiveness, rather than on their overall performance at multi-sensor alignment or recovery. We present the data on the performance of each pair under all transformations independently: translations and rotations in and around each of the x-, y- and z-axis respectively. We provide insight into the implementation of the detectors and descriptors in PCL leading to abnormal or unexpected performance. The obtained results show that the ISS/SHOT and ISS/SHOTColor detector/descriptor pair works best at 3D recovery under various transformations.

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References

  1. PCL: Pairwise registraion - point cloud library (2016). http://pointclouds.org/documentation/tutorials/registration_api.php. Accessed 15 May 2016

  2. Mitra, N.J., Gelfand, N., Pottmann, H., Guibas, L.: Registration of point cloud data from a geometric optimization perspective. In: Proceedings of the Eurographics/ACM SIGGRAPH Symposium on Geometry Processing, pp. 22–31. ACM (2004)

    Google Scholar 

  3. PCL: Point cloud library (2016). http://pointclouds.org/. Accessed 15 May 2016

  4. Sipiran, I., Bustos, B.: Harris 3D: a robust extension of the harris operator for interest point detection on 3D meshes. Vis. Comput. 27, 963 (2011)

    Article  Google Scholar 

  5. Zhong, Y.: Intrinsic shape signatures: a shape descriptor for 3D object recognition. In: IEEE 12th International Conference on Computer Vision Workshops, pp. 689–696 (2009)

    Google Scholar 

  6. Filipe, S., Alexandre, L.A.: A comparative evaluation of 3D keypoint detectors in a RGB-D object dataset. In: International Conference on Computer Vision Theory and Applications, vol. 1, pp. 476–483. IEEE (2014)

    Google Scholar 

  7. Smith, S.M., Brady, J.M.: SUSAN—a new approach to low level image processing. Int. J. Comput. Vis. 23, 45–78 (1997)

    Article  Google Scholar 

  8. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 15, p. 50. Citeseer (1988)

    Google Scholar 

  9. Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of IEEE International Conference on Computer Vision, pp. 1150–1157 (1999)

    Google Scholar 

  10. Steder, B., Rusu, R.B., Konolige, K., Burgard, W.: NARF: 3D range image features for object recognition. In: Workshop on Defining and Solving Realistic Perception Problems in Personal Robotics at the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 44 (2010)

    Google Scholar 

  11. Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 183–196. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15552-9_14

    Chapter  Google Scholar 

  12. Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision, pp. 2548–2555 (2011)

    Google Scholar 

  13. Rusu, R.B., Blodow, N., Marton, Z.C., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3384–3391 (2008)

    Google Scholar 

  14. Rusu, R.B., Marton, Z.C., Blodow, N., Beetz, M.: Learning informative point classes for the acquisition of object model maps. In: 10th International Conference on Control, Automation, Robotics and Vision, pp. 643–650. IEEE (2008)

    Google Scholar 

  15. Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation, pp. 3212–3217. IEEE (2009)

    Google Scholar 

  16. Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J.: Fast 3D recognition and pose using the view point feature histogram. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2155–2162 (2010)

    Google Scholar 

  17. Aldoma, A., Vincze, M., Blodow, N., Gossow, D., Gedikli, S., Rusu, R.B., Bradski, G.: CAD-model recognition and 6DOF pose estimation using 3D cues. In: IEEE International Conference on Computer Vision Workshops, pp. 585–592 (2011)

    Google Scholar 

  18. Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1265–1278 (2005)

    Article  Google Scholar 

  19. Tombari, F., Salti, S., Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 356–369. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15558-1_26

    Chapter  Google Scholar 

  20. Tombari, F., Salti, S., Stefano, L.D.: A combined texture-shape descriptor for enhanced 3D feature matching. In: 18th IEEE International Conference on Image Processing, pp. 809–812 (2011)

    Google Scholar 

  21. Alexandre, L.A.: 3D descriptors for object and category recognition: a comparative evaluation. In: Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal, vol. 1, p. 7. Citeseer (2012)

    Google Scholar 

  22. Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view RGB-D object dataset. In: IEEE International Conference on Robotics and Automation, pp. 1817–1824 (2011)

    Google Scholar 

  23. Hänsch, R., Weber, T., Hellwich, O.: Comparison of 3D interest point detectors and descriptors for point cloud fusion. ISPRS Ann. Photogrammetry Remote Sens. Spatial Inf. Sci. 2, 57 (2014)

    Article  Google Scholar 

  24. Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3D objects. Int. J. Comput. Vis. 73, 263–284 (2007)

    Article  Google Scholar 

  25. Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Application, pp. 331–340 (2009)

    Google Scholar 

  26. Muja, M., Lowe, D.G.: Fast matching of binary features. In: Computer and Robot Vision, pp. 404–410 (2012)

    Google Scholar 

  27. Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36 (2014)

    Google Scholar 

  28. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  29. ACENET: Acenet - advanced computing research in Atlantic Canada (2016). http://www.ace-net.ca. Accessed 15 May 2016

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Author information

Authors and Affiliations

  1. Faculty of Science, Memorial University of Newfoundland, St. John’s, A1B 3X9, Canada

    Zizui Chen

  2. Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, A1B 3X9, Canada

    Stephen Czarnuch & Mohamed Shehata

  3. Faculty of Medicine, Memorial University of Newfoundland, St. John’s, A1B 3X9, Canada

    Stephen Czarnuch & Andrew Smith

Authors
  1. Zizui Chen
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  2. Stephen Czarnuch
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  3. Andrew Smith
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  4. Mohamed Shehata
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Corresponding author

Correspondence to Stephen Czarnuch .

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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© 2016 Springer International Publishing AG

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Chen, Z., Czarnuch, S., Smith, A., Shehata, M. (2016). Performance Evaluation of 3D Keypoints and Descriptors. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_40

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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