Advertisement

On spatio-temporal feature point detection for animated meshes

  • 215 Accesses

  • 1 Citations

Abstract

Although automatic feature detection has been a long-sought subject by researchers in computer graphics and computer vision, feature extraction on deforming models remains a relatively unexplored area. In this paper, we develop a new method for automatic detection of spatio-temporal feature points on animated meshes. Our algorithm consists of three main parts. We first define local deformation characteristics, based on strain and curvature values computed for each point at each frame. Next, we construct multi-resolution space–time Gaussians and difference-of-Gaussian (DoG) pyramids on the deformation characteristics representing the input animated mesh, where each level contains 3D smoothed and subsampled representation of the previous level. Finally, we estimate locations and scales of spatio-temporal feature points by using a scale-normalized differential operator. A new, precise approximation of spatio-temporal scale-normalized Laplacian has been introduced, based on the space–time DoG. We have experimentally verified our algorithm on a number of examples and conclude that our technique allows to detect spatio and temporal feature points in a reliable manner.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 199

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

References

  1. 1.

    Andonie, R., Carai, E.: Gaussian smoothing by optimal iterated uniform convolutions. Comput. Artif. Intell. 11(4), 363–373 (1992)

  2. 2.

    Alliez, P., Cohen-Steiner, D., Devillers, O., Levy, B., Desbrun, M.: Anisotropic Polygonal Remeshing. ACM Trans. Graph. 22(3), 485–495 (2003)

  3. 3.

    Bay, H., Ess, A., Tuytelaars, T., van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. (CVIU) 110(3), 346–359 (2008)

  4. 4.

    Castellani, U., Cristani, M., Fantoni, S., Murino, V.: Sparse points matching by combining 3D mesh saliency with statistical descriptors. Comput. Graph. Forum 27(2), 643–652 (2008)

  5. 5.

    Darom, T., Keller, Y.: Scale-Invariant features for 3-D mesh models. IEEE Trans. Image Process. 21(5), 2758–2769 (2012)

  6. 6.

    Gotoh, O.: Optimal sequence alignment allowing for long gaps. Bull. Math. Biol. 52, 359–373 (1990)

  7. 7.

    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of 4th Alvey Vision Conference, pp. 147–151 (1988)

  8. 8.

    Kircher, S., Garland, M.: Progressive multiresolution meshes for deforming surfaces. In: ACM SIGGRAPH Symposium on Computer, Animation, pp. 191–200 (2005)

  9. 9.

    Lian, Z., Godil, A., Xiao, J.: Feature-preserved 3D canonical form. Int. J. Comput. Vis. 102, 221–238 (2013)

  10. 10.

    Laptev, I., Lindeberg, T.: Interest point detection and scale selection in space-time. In: Griffin, L.D., Lilholm, M. (eds.) Scale Space Methods in Computer Vision. Lecture Notes in Computer Science, vol. 2695, pp. 372–387. Springer, Berlin, Heidelberg (2003)

  11. 11.

    Lindeberg, T.: Scale-space theory: a basic tool for analyzing structures at different scales. J. Appl. Stat. 21(2), 224–270 (1994)

  12. 12.

    Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comput. Vis. 30, 79–116 (1998)

  13. 13.

    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

  14. 14.

    Lee, C.-H., Varshney, A., Jacobs, D.W.: Mesh Saliency, ACM Transactions on Graphics. Proc, SIGGRAPH (2005)

  15. 15.

    Mesh data, http://people.csail.mit.edu/sumner/research/deftransfer/data.html

  16. 16.

    Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. IEEE Int Conf. Comput. Vis. (ICCV), pp. 525–531 (2001)

  17. 17.

    Pauly, M., Keiser, R., Gross, M.: Multi-scale feature extraction on point-sampled surfaces. Comput. Graph. Forum 22(3), 281–289 (2003)

  18. 18.

    P. Scovanner, S. Ali, and M. Shah: A 3-Dimensional SIFT Descriptor and Its Application to Action Recognition. Proceedings of the 15th International Conference on Multimedia, pp. 357–360, (2007)

  19. 19.

    Sipiran, I., Bustos, B.: A robust 3D interest points detector based on harris operator. Eurographics workshop on 3D Object Retrieval (3DOR), pp. 7–14 (2010)

  20. 20.

    Sumner, R., Popovic, J.: Deformation transfer for triangle meshes. ACM Trans. Grap. 23, 3 (2004)

  21. 21.

    Shamir, A., Pascucci, V., Bajaj, C.: Multi-resolution dynamic meshes with arbitrary deformations. In: Proceedings of IEEE Visualization, pp. 423–430 (2000)

  22. 22.

    Salden, A.H., ter Haar Romeny, B.M., Viergever, M.A.: Linear scale-space theory from physical principles. J. Math. Imaging Vis. 9, 103–139 (1998)

  23. 23.

    Sumner, R.W., Zwicker, M., Gotsman, C., Popović, J.: Mesh-based inverse kinematics. ACM Trans. Graph. (TOG) 24(3), 488–495 (2005)

  24. 24.

    Vicon motion capture system, http://vicon.com

  25. 25.

    Zaharescu, A., Boyer, E., Varanasi, K., Horaud, R.: Surface feature detection and description with applications to mesh matching. IEEE Comput. Vis. Pattern Recognit. (CVPR), pp. 373–380 (2009)

Download references

Acknowledgments

We acknowledge Robert W. Sumner for providing triangle correspondences of the horse and camel models. We also thank Frederic Larue and Olivier Génevaux for their assistance with the facial motion capture. This work has been supported by the French national project SHARED (Shape Analysis and Registration of People Using Dynamic Data, No.10-CHEX-014-01).

Author information

Correspondence to Hyewon Seo.

Electronic supplementary material

Below is the link to the electronic supplementary material.

371_2014_1027_MOESM1_ESM

371_2014_1027_MOESM1_ESM

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mykhalchuk, V., Seo, H. & Cordier, F. On spatio-temporal feature point detection for animated meshes. Vis Comput 31, 1471–1486 (2015). https://doi.org/10.1007/s00371-014-1027-1

Download citation

Keywords

  • Feature detection
  • Animated mesh
  • Multi-scale representation
  • Difference of Gaussian