Evaluation of Local 3-D Point Cloud Descriptors in Terms of Suitability for Object Classification

Jens Garstka, Gabriele Peters

2016

Abstract

This paper investigates existing methods for local 3-D feature description with special regards to their suitability for object classification based on 3-D point cloud data. We choose five approved descriptors, namely Spin Images, Point Feature Histogram, Fast Point Feature Histogram, Signature of Histograms of Orientations, and Unique Shape Context and evaluate them with a commonly used classification pipeline on a large scale 3-D object dataset comprising more than 200000 different point clouds. Of particular interest are the details of the choice of all parameters associated with the classification pipeline. The point clouds are classified by using support vector machines. Fast Point Feature Histogram proves to be the best descriptor for the method of object classification used in this evaluation.

References

  1. Aldoma, A., Marton, Z.-C., Tombari, F., Wohlkinger, W., Potthast, C., Zeisl, B., Rusu, R., Gedikli, S., and Vincze, M. (2012a). Tutorial: Point cloud library: Three-dimensional object recognition and 6 dof pose estimation. Robotics Automation Magazine, IEEE, 19(3):80-91.
  2. Aldoma, A., Tombari, F., Rusu, R. B., and Vincze, M. (2012b). Our-cvfh-oriented, unique and repeatable clustered viewpoint feature histogram for object recognition and 6dof pose estimation. In Pattern Recognition, pages 113-122. Springer.
  3. Alexandre, L. A. (2012). 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.
  4. Arbeiter, G., Fuchs, S., Bormann, R., Fischer, J., and Verl, A. (2012). Evaluation of 3d feature descriptors for classification of surface geometries in point clouds. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012, Vilamoura, Algarve, Portugal, October 7-12, 2012, pages 1644- 1650.
  5. Arthur, D. and Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pages 1027-1035. Society for Industrial and Applied Mathematics.
  6. Cholewa, M. and Sporysz, P. (2014). Classification of dynamic sequences of 3d point clouds. In Artificial Intelligence and Soft Computing, pages 672-683. Springer.
  7. Drost, B., Ulrich, M., Navab, N., and Ilic, S. (2010). Model globally, match locally: Efficient and robust 3d object recognition. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 998- 1005. IEEE.
  8. Dutagaci, H., Cheung, C. P., and Godil, A. (2012). Evaluation of 3d interest point detection techniques via human-generated ground truth. The Visual Computer, 28(9):901-917.
  9. Filipe, S. and Alexandre, L. A. (2013). A Comparative Evaluation of 3D Keypoint Detectors. In 9th Conference on Telecommunications, Conftele 2013, pages 145-148, Castelo Branco, Portugal.
  10. Frome, A., Huber, D., Kolluri, R., Bulow, T., and Malik, J. (2004). Recognizing objects in range data using regional point descriptors. In Proceedings of the European Conference on Computer Vision (ECCV).
  11. Guo, Y., Bennamoun, M., Sohel, F., Lu, M., and Wan, J. (2014). 3d object recognition in cluttered scenes with local surface features: A survey. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(11):2270-2287.
  12. Heider, P., Pierre-Pierre, A., Li, R., and Grimm, C. (2011). Local shape descriptors, a survey and evaluation. In Proceedings of the 4th Eurographics conference on 3D Object Retrieval, pages 49-56. Eurographics Association.
  13. Johnson, A. and Hebert, M. (1999). Using spin images for efficient object recognition in cluttered 3d scenes.Pattern Analysis and Machine Intelligence, IEEE Transactions on, 21(5):433-449.
  14. Johnson, A. E. and Hebert, M. (1998). Surface matching for object recognition in complex three-dimensional scenes. Image and Vision Computing, 16(9):635-651.
  15. Kim, H. and Hilton, A. (2013). Evaluation of 3d feature descriptors for multi-modal data registration. In 2013 International Conference on 3D Vision, 3DV 2013, Seattle, Washington, USA, June 29 - July 1, 2013, pages 119-126.
  16. Knopp, J., Prasad, M., Willems, G., Timofte, R., and Van Gool, L. (2010). Hough transform and 3d surf for robust three dimensional classification. In Computer Vision-ECCV 2010 , pages 589-602. Springer.
  17. Lai, K., Bo, L., Ren, X., and Fox, D. (2011). A largescale hierarchical multi-view rgb-d object dataset. In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 1817-1824. IEEE.
  18. Lian, Z., Godil, A., Bustos, B., Daoudi, M., Hermans, J., Kawamura, S., Kurita, Y., Lavoué, G., Van Nguyen, H., Ohbuchi, R., et al. (2011). Shrec'11 track: Shape retrieval on non-rigid 3d watertight meshes. 3DOR, 11:79-88.
  19. Madry, M., Afkham, H. M., Ek, C. H., Carlsson, S., and Kragic, D. (2013). Extracting essential local object characteristics for 3d object categorization. In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, pages 2240-2247. IEEE.
  20. Madry, M., Ek, C. H., Detry, R., Hang, K., and Kragic, D. (2012). Improving generalization for 3d object categorization with global structure histograms. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pages 1379-1386. IEEE.
  21. Rusu, R., Blodow, N., and Beetz, M. (2009). Fast point feature histograms (fpfh) for 3d registration. In Robotics and Automation, 2009. ICRA 7809. IEEE International Conference on, pages 3212-3217.
  22. Rusu, R. B. (2010). Semantic 3d object maps for everyday manipulation in human living environments. KIKünstliche Intelligenz, 24(4):345-348.
  23. Rusu, R. B., Blodow, N., Marton, Z. C., and Beetz, M. (2008a). Aligning point cloud views using persistent feature histograms. In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, pages 3384-3391. IEEE.
  24. Rusu, R. B., Marton, Z. C., Blodow, N., and Beetz, M. (2008b). Learning informative point classes for the acquisition of object model maps. In Control, Automation, Robotics and Vision, 2008. ICARCV 2008. 10th International Conference on, pages 643-650. IEEE.
  25. Salti, S., Tombari, F., and Stefano, L. D. (2011). A performance evaluation of 3d keypoint detectors. In 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2011 International Conference on, pages 236-243. IEEE.
  26. Salti, S., Tombari, F., and Stefano, L. D. (2014). Shot: Unique signatures of histograms for surface and texture description. Computer Vision and Image Understanding, 125(0):251 - 264.
  27. Seib, V., Christ-Friedmann, S., Thierfelder, S., and Paulus, D. (2013). Object class and instance recognition on rgb-d data. In Sixth International Conference on Machine Vision (ICMV 13), pages 90670J-90670J. International Society for Optics and Photonics.
  28. Tang, S. and Godil, A. (2012). An evaluation of local shape descriptors for 3d shape retrieval. CoRR, abs/1202.2368.
  29. Toldo, R., Castellani, U., and Fusiello, A. (2009). A bag of words approach for 3d object categorization. In Computer Vision/Computer Graphics CollaborationTechniques, pages 116-127. Springer.
  30. Toldo, R., Castellani, U., and Fusiello, A. (2010). The bag of words approach for retrieval and categorization of 3d objects. The Visual Computer, 26(10):1257-1268.
  31. Tombari, F., Salti, S., and Di Stefano, L. (2010a). Unique shape context for 3d data description. In Proceedings of the ACM workshop on 3D object retrieval, pages 57-62. ACM.
  32. Tombari, F., Salti, S., and Di Stefano, L. (2010b). Unique signatures of histograms for local surface description. In Computer Vision-ECCV 2010 , pages 356- 369. Springer.
  33. Wu, C.-C. and Lin, S.-F. (2011). Efficient model detection in point cloud data based on bag of words classification. Journal of Computational Information Systems, 7(12):4170-4177.
  34. Yi, Y., Guang, Y., Hao, Z., Meng-Yin, F., and Mei-ling, W. (2014). Object segmentation and recognition in 3d point cloud with language model. In Multisensor Fusion and Information Integration for Intelligent Systems (MFI), 2014 International Conference on, pages 1-6. IEEE.
  35. Zhong, Y. (2009). Intrinsic shape signatures: A shape descriptor for 3d object recognition. In Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, pages 689-696. IEEE.
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Paper Citation


in Harvard Style

Garstka J. and Peters G. (2016). Evaluation of Local 3-D Point Cloud Descriptors in Terms of Suitability for Object Classification . In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-198-4, pages 540-547. DOI: 10.5220/0006011505400547


in Bibtex Style

@conference{icinco16,
author={Jens Garstka and Gabriele Peters},
title={Evaluation of Local 3-D Point Cloud Descriptors in Terms of Suitability for Object Classification},
booktitle={Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2016},
pages={540-547},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006011505400547},
isbn={978-989-758-198-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Evaluation of Local 3-D Point Cloud Descriptors in Terms of Suitability for Object Classification
SN - 978-989-758-198-4
AU - Garstka J.
AU - Peters G.
PY - 2016
SP - 540
EP - 547
DO - 10.5220/0006011505400547