Advertisement

3D Object Pose Estimation Using Viewpoint Generative Learning

  • Dissaphong Thachasongtham
  • Takumi Yoshida
  • François de Sorbier
  • Hideo Saito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

Conventional local features such as SIFT or SURF are robust to scale and rotation changes but sensitive to large perspective change. Because perspective change always occurs when 3D object moves, using these features to estimate the pose of a 3D object is a challenging task. In this paper, we extend one of our previous works on viewpoint generative learning to 3D objects. Given a model of a textured object, we virtually generate several patterns of the model from different viewpoints and select stable keypoints from those patterns. Then our system learns a collection of feature descriptors from the stable keypoints. Finally, we are able to estimate the pose of a 3D object by using these robust features. In our experimental results, we demonstrate that our system is robust against large viewpoint change and even under partial occlusion.

Keywords

pose estimation generative learning stable keypoint 

References

  1. 1.
    Pilet, J., Saito, H.: Virtually augmenting hundreds of real pictures: An approach based on learning, retrieval, and tracking. In: Proceedings of the 2010 IEEE Virtual Reality Conference, pp. 71–78 (2010)Google Scholar
  2. 2.
    Uchiyama, H., Marchand, E.: Toward augmenting everything: Detecting and tracking geometrical features on planar objects. In: Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pp. 17–25 (2011)Google Scholar
  3. 3.
    Vacchetti, L., Lepetit, V., Fua, P.: Stable Real-Time 3D Tracking Using Online and Offline Information. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1385–1391 (2004)CrossRefGoogle Scholar
  4. 4.
    Park, Y., Lepetit, V., Woontack, W.: Multiple 3D Object tracking for augmented reality. In: Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality, pp. 117–120 (2008)Google Scholar
  5. 5.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  7. 7.
    Yoshida, T., Saito, H., Shimizu, M., Taguchi, A.: Stable keypoint recognition using viewpoint generative learning. In: Proceedings of the 8th International Conference on Computer Vision Theory and Applications, vol. 2, pp. 310–315 (2013)Google Scholar
  8. 8.
    Hirose, R., Saito, H.: A vision-based AR registration method utilizing edges and vertices of 3D model. In: Proceedings of the 2005 International Conference on Augmented Tele-Existence, pp. 187–194 (2005)Google Scholar
  9. 9.
    Park, Y., Lepetit, V., Woo, W.: Texture-less object tracking with online training using an RGB-D camera. In: Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pp. 121–126 (2011)Google Scholar
  10. 10.
    Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J.: Fast 3D recognition and pose using the Viewpoint Feature Histogram. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2155–2162 (2010)Google Scholar
  11. 11.
    Lepetit, V., Fua, P.: Keypoint Recognition Using Randomized Trees. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1465–1479 (2006)CrossRefGoogle Scholar
  12. 12.
    Daniel, K., Thomas, O., Selim, B.: Representative feature descriptor sets for robust handheld camera localization. In: Proceedings of the 2012 11th IEEE International Symposium on Mixed and Augmented Reality, pp. 65–70 (2012)Google Scholar
  13. 13.
    Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035 (2007)Google Scholar
  14. 14.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A Comparison of Affine Region Detectors. Int. J. Comput. Vision 65(1-2), 43–72 (2005)CrossRefGoogle Scholar
  15. 15.
    Moreno-Noguer, F., Lepetit, V., Fua, P.: Accurate Non-Iterative O(n) Solution to the PnP Problem. In: Proceedings of the International Conference on Computer Vision (2007)Google Scholar
  16. 16.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dissaphong Thachasongtham
    • 1
  • Takumi Yoshida
    • 1
  • François de Sorbier
    • 1
  • Hideo Saito
    • 1
  1. 1.Graduate School of Science and TechnologyKeio University YokohamaJapan

Personalised recommendations