TriSI: A Distinctive Local Surface Descriptor for 3D Modeling and Object Recognition

Yulan Guo, Ferdous Sohel, Mohammed Bennamoun, Min Lu, Jianwei Wan

2013

Abstract

Local surface description is a critical stage for surface matching. This paper presents a highly distinctive local surface descriptor, namely TriSI. From a keypoint, we first construct a unique and repeatable local reference frame (LRF) using all the points lying on the local surface. We then generate three spin images from the three coordinate axes of the LRF. These spin images are concatenated and further compressed into a TriSI descriptor using the principal component analysis technique. We tested our TriSI descriptor on the Bologna Dataset and compared it to several existing methods. Experimental results show that TriSI outperformed existing methods under all levels of noise and varying mesh resolutions. The TriSI was further tested to demonstrate its effectiveness in 3D modeling. Experimental results show that it can accurately perform pairwise and multiview range image registration. We finally used the TriSI descriptor for 3D object recognition. The results on the UWA Dataset show that TriSI outperformed the state-of-the-art methods including spin image, tensor and exponential map. The TriSI based method achieved a high recognition rate of 98.4%.

References

  1. Attene, M., Marini, S., Spagnuolo, M., and Falcidieno, B. (2011). Part-in-whole 3D shape matching and docking. The Visual Computer, 27(11):991-1004.
  2. Bariya, P., Novatnack, J., Schwartz, G., and Nishino, K. (2012). 3D geometric scale variability in range images: Features and descriptors. International Journal of Computer Vision, 99(2):232-255.
  3. Boyer, E., Bronstein, A., Bronstein, M., Bustos, B., Darom, T., Horaud, R., Hotz, I., Keller, Y., Keustermans, J., Kovnatsky, A., et al. (2011). SHREC 2011: Robust feature detection and description benchmark. In Eurographics Workshop on Shape Retrieval, pages 79-86.
  4. Bronstein, A., Bronstein, M., Bustos, B., Castellani, U., Crisani, M., Falcidieno, B., Guibas, L., Kokkinos, I., Murino, V., Ovsjanikov, M., et al. (2010). SHREC 2010: robust feature detection and description benchmark. In Eurographics Workshop on 3D Object Retrieval, volume 2, page 6.
  5. Bustos, B., Keim, D., Saupe, D., Schreck, T., and Vranic, D. (2005). Feature-based similarity search in 3D object databases. ACM Computing Surveys, 37(4):345-387.
  6. Chen, H. and Bhanu, B. (2007). 3D free-form object recognition in range images using local surface patches. Pattern Recognition Letters, 28(10):1252-1262.
  7. Curless, B. and Levoy, M. (1996). A volumetric method for building complex models from range images. In 23rd Annual Conference on Computer Graphics and Interactive Techniques, pages 303-312.
  8. Frome, A., Huber, D., Kolluri, R., Bülow, T., and Malik, J. (2004). Recognizing objects in range data using regional point descriptors. In 8th European Conference on Computer Vision, pages 224-237.
  9. Guo, Y., Bennamoun, M., Sohel, F., Wan, J., and Lu, M. (2013). 3D free form object recognition using rotational projection statistics. In IEEE 14th Workshop on the Applications of Computer Vision. In press.
  10. Guo, Y., Wan, J., Lu, M., and Niu, W. (2012). A parts-based method for articulated target recognition in laser radar data. Optik. http://dx.doi.org/10.1016/j.ijleo.2012.08.035.
  11. Hetzel, G., Leibe, B., Levi, P., and Schiele, B. (2001). 3D object recognition from range images using local feature histograms. In IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages II394.
  12. Huber, D., Carmichael, O., and Hebert, M. (2000). 3D map reconstruction from range data. In IEEE International Conference on Robotics and Automation, volume 1, pages 891-897. IEEE.
  13. Johnson, A. E. and Hebert, M. (1999). Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5):433-449.
  14. Ke, Y. and Sukthankar, R. (2004). PCA-SIFT: A more distinctive representation for local image descriptors. In IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 498-506.
  15. Lai, K., Bo, L., Ren, X., and Fox, D. (2011a). A largescale hierarchical multi-view GRB-D object dataset. In IEEE International Conference on Robotics and Automation, pages 1817-1824.
  16. Lai, K., Bo, L., Ren, X., and Fox, D. (2011b). A scalable tree-based approach for joint object and pose recognition. In Twenty-Fifth Conference on Artificial Intelligence (AAAI).
  17. Mian, A., Bennamoun, M., and Owens, R. (2005). Automatic correspondence for 3D modeling: An extensive review. International Journal of Shape Modeling, 11(2):253.
  18. Mian, A., Bennamoun, M., and Owens, R. (2006). Threedimensional model-based object recognition and segmentation in cluttered scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10):1584-1601.
  19. Mian, A., Bennamoun, M., and Owens, R. (2010). On the repeatability and quality of keypoints for local featurebased 3D object retrieval from cluttered scenes. International Journal of Computer Vision, 89(2):348-361.
  20. Novatnack, J. and Nishino, K. (2008). Scale-dependent/ invariant local 3D shape descriptors for fully automatic registration of multiple sets of range images. In 10th European Conference on Computer Vision, pages 440-453.
  21. Rusu, R. and Cousins, S. (2011). 3D is here: Point cloud library (pcl). In 2011 IEEE International Conference on Robotics and Automation, pages 1-4.
  22. Salti, S., Tombari, F., and Stefano, L. (2011). A performance evaluation of 3D keypoint detectors. In International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, pages 236- 243.
  23. Shilane, P., Min, P., Kazhdan, M., and Funkhouser, T. (2004). The Princeton shape benchmark. In International Conference on Shape Modeling Applications, pages 167-178.
  24. Sun, Y. and Abidi, M. (2001). Surface matching by 3D point's fingerprint. In 8th IEEE International Conference on Computer Vision, volume 2, pages 263-269.
  25. Taati, B. and Greenspan, M. (2011). Local shape descriptor selection for object recognition in range data. Computer Vision and Image Understanding, 115(5):681- 694.
  26. Tombari, F., Salti, S., and Di Stefano, L. (2010). Unique signatures of histograms for local surface description. In European Conference on Computer Vision, pages 356-369.
  27. Williams, J. and Bennamoun, M. (2000). A multiple view 3D registration algorithm with statistical error modeling. IEICE Transactions on Information and Systems, 83(8):1662-1670.
  28. Williams, J. and Bennamoun, M. (2001). Simultaneous registration of multiple corresponding point sets. Computer Vision and Image Understanding, 81(1):117- 142.
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Paper Citation


in Harvard Style

Guo Y., Sohel F., Bennamoun M., Lu M. and Wan J. (2013). TriSI: A Distinctive Local Surface Descriptor for 3D Modeling and Object Recognition . In Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2013) ISBN 978-989-8565-46-4, pages 86-93. DOI: 10.5220/0004277600860093


in Bibtex Style

@conference{grapp13,
author={Yulan Guo and Ferdous Sohel and Mohammed Bennamoun and Min Lu and Jianwei Wan},
title={TriSI: A Distinctive Local Surface Descriptor for 3D Modeling and Object Recognition},
booktitle={Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2013)},
year={2013},
pages={86-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004277600860093},
isbn={978-989-8565-46-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2013)
TI - TriSI: A Distinctive Local Surface Descriptor for 3D Modeling and Object Recognition
SN - 978-989-8565-46-4
AU - Guo Y.
AU - Sohel F.
AU - Bennamoun M.
AU - Lu M.
AU - Wan J.
PY - 2013
SP - 86
EP - 93
DO - 10.5220/0004277600860093