Geometric Edge Description and Classification in Point Cloud Data with Application to 3D Object Recognition

Troels Bo Jørgensen, Anders Glent Buch, Dirk Kraft

2015

Abstract

This paper addresses the detection of geometric edges on 3D shapes. We investigate the use of local point cloud features and cast the edge detection problem as a learning problem. We show how supervised learning techniques can be applied to an existing shape description in terms of local feature descriptors. We apply our approach to several well-known shape descriptors. As an additional contribution, we develop a novel shape descriptor, termed Equivalent Circumference Surface Angle Descriptor or ECSAD, which is particularly suitable for capturing local surface properties near edges. Our proposed descriptor allows for both fast computation and fast processing by having a low dimension, while still producing highly reliable edge detections. Lastly, we use our features in a 3D object recognition application using a well-established benchmark. We show that our edge features allow for significant speedups while achieving state of the art results.

References

  1. Bähnisch, C., Stelldinger, P., and Köthe, U. (2009). Fast and accurate 3D edge detection for surface reconstruction. In Pattern Recognition, pages 111-120. Springer.
  2. Besl, P. and McKay, N. D. (1992). A method for registration of 3-d shapes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 14(2):239-256.
  3. Breiman, L. (2001). Random forests. Machine Learning, 45(1):5-32.
  4. Buch, A. G., Jessen, J. B., Kraft, D., Savarimuthu, T. R., and Krüger, N. (2013a). Extended 3D line segments from RGB-D data for pose estimation. In Scandinavian Conference on Image Analysis (SCIA), pages 54- 65. Springer.
  5. Buch, A. G., Kraft, D., Kamarainen, J.-K., Petersen, H. G., and Kruger, N. (2013b). Pose estimation using local structure-specific shape and appearance context. In Robotics and Automation (ICRA), 2013 IEEE International Conference on, pages 2080-2087.
  6. Canny, J. (1986). A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI-8(6):679-698.
  7. Choi, C., Trevor, A. J., and Christensen, H. I. (2013). RGBD edge detection and edge-based registration. In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, pages 1568-1575.
  8. 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.
  9. Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395.
  10. Frome, A., Huber, D., Kolluri, R., Bülow, T., and Malik, J. (2004). Recognizing objects in range data using regional point descriptors. In Proceedings of the European Conference on Computer Vision (ECCV), pages 224-237.
  11. Gumhold, S., Wang, X., and MacLeod, R. (2001). Feature extraction from point clouds. In Proceedings of 10th international meshing roundtable, pages 293-305.
  12. Guy, G. and Medioni, G. (1997). Inference of surfaces, 3D curves, and junctions from sparse, noisy, 3D data. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(11):1265-1277.
  13. Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., and Stuetzle, W. (1992). Surface reconstruction from unorganized points. In ACM SIGGRAPH Proceedings, pages 71-78.
  14. Johnson, A. E. 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.
  15. 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.
  16. Mian, A. S., Bennamoun, M., and Owens, R. (2006). Three-dimensional model-based object recognition and segmentation in cluttered scenes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(10):1584-1601.
  17. Mikolajczyk, K. and Schmid, C. (2005). A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10):1615-1630.
  18. Monga, O., Deriche, R., and Rocchisani, J.-M. (1991). 3D edge detection using recursive filtering: application to scanner images. CVGIP: Image Understanding, 53(1):76-87.
  19. Pauly, M., Gross, M., and Kobbelt, L. P. (2002). Efficient simplification of point-sampled surfaces. In IEEE Conference on Visualization, pages 163-170.
  20. Pauly, M., Keiser, R., and Gross, M. (2003). Multi-scale feature extraction on point-sampled surfaces. Computer Graphics Forum, 22(3):281-289.
  21. Rusu, R. B., Blodow, N., and Beetz, M. (2009). Fast point feature histograms (FPFH) for 3D registration. In Robotics and Automation, 2009. ICRA'09. IEEE International Conference on, pages 3212-3217.
  22. Rusu, R. B. and Cousins, S. (2011). 3D is here: Point cloud library (PCL). In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 1-4.
  23. Tombari, F., Salti, S., and Di Stefano, L. (2010). Unique signatures of histograms for local surface description. In European Conference on Computer Vision (ECCV), pages 356-369.
Download


Paper Citation


in Harvard Style

Jørgensen T., Buch A. and Kraft D. (2015). Geometric Edge Description and Classification in Point Cloud Data with Application to 3D Object Recognition . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 333-340. DOI: 10.5220/0005196703330340


in Bibtex Style

@conference{visapp15,
author={Troels Bo Jørgensen and Anders Glent Buch and Dirk Kraft},
title={Geometric Edge Description and Classification in Point Cloud Data with Application to 3D Object Recognition},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={333-340},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005196703330340},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Geometric Edge Description and Classification in Point Cloud Data with Application to 3D Object Recognition
SN - 978-989-758-089-5
AU - Jørgensen T.
AU - Buch A.
AU - Kraft D.
PY - 2015
SP - 333
EP - 340
DO - 10.5220/0005196703330340