The Visual Computer

, Volume 31, Issue 11, pp 1471–1486 | Cite as

On spatio-temporal feature point detection for animated meshes

  • Vasyl Mykhalchuk
  • Hyewon SeoEmail author
  • Frederic Cordier
Original Article


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.


Feature detection Animated mesh  Multi-scale representation Difference of Gaussian 



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).

Supplementary material



  1. 1.
    Andonie, R., Carai, E.: Gaussian smoothing by optimal iterated uniform convolutions. Comput. Artif. Intell. 11(4), 363–373 (1992)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Alliez, P., Cohen-Steiner, D., Devillers, O., Levy, B., Desbrun, M.: Anisotropic Polygonal Remeshing. ACM Trans. Graph. 22(3), 485–495 (2003)Google Scholar
  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)CrossRefGoogle Scholar
  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)CrossRefGoogle Scholar
  5. 5.
    Darom, T., Keller, Y.: Scale-Invariant features for 3-D mesh models. IEEE Trans. Image Process. 21(5), 2758–2769 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Gotoh, O.: Optimal sequence alignment allowing for long gaps. Bull. Math. Biol. 52, 359–373 (1990)zbMATHCrossRefGoogle Scholar
  7. 7.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of 4th Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  8. 8.
    Kircher, S., Garland, M.: Progressive multiresolution meshes for deforming surfaces. In: ACM SIGGRAPH Symposium on Computer, Animation, pp. 191–200 (2005)Google Scholar
  9. 9.
    Lian, Z., Godil, A., Xiao, J.: Feature-preserved 3D canonical form. Int. J. Comput. Vis. 102, 221–238 (2013)CrossRefGoogle Scholar
  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)Google Scholar
  11. 11.
    Lindeberg, T.: Scale-space theory: a basic tool for analyzing structures at different scales. J. Appl. Stat. 21(2), 224–270 (1994)Google Scholar
  12. 12.
    Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comput. Vis. 30, 79–116 (1998)CrossRefGoogle Scholar
  13. 13.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Lee, C.-H., Varshney, A., Jacobs, D.W.: Mesh Saliency, ACM Transactions on Graphics. Proc, SIGGRAPH (2005)Google Scholar
  15. 15.
  16. 16.
    Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. IEEE Int Conf. Comput. Vis. (ICCV), pp. 525–531 (2001)Google Scholar
  17. 17.
    Pauly, M., Keiser, R., Gross, M.: Multi-scale feature extraction on point-sampled surfaces. Comput. Graph. Forum 22(3), 281–289 (2003)CrossRefGoogle Scholar
  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)Google Scholar
  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)Google Scholar
  20. 20.
    Sumner, R., Popovic, J.: Deformation transfer for triangle meshes. ACM Trans. Grap. 23, 3 (2004)Google Scholar
  21. 21.
    Shamir, A., Pascucci, V., Bajaj, C.: Multi-resolution dynamic meshes with arbitrary deformations. In: Proceedings of IEEE Visualization, pp. 423–430 (2000)Google Scholar
  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)zbMATHCrossRefGoogle Scholar
  23. 23.
    Sumner, R.W., Zwicker, M., Gotsman, C., Popović, J.: Mesh-based inverse kinematics. ACM Trans. Graph. (TOG) 24(3), 488–495 (2005)CrossRefGoogle Scholar
  24. 24.
    Vicon motion capture system,
  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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Vasyl Mykhalchuk
    • 1
  • Hyewon Seo
    • 1
    Email author
  • Frederic Cordier
    • 2
  1. 1.University of StrasbourgStrasbourgFrance
  2. 2.University of Haute AlsaceMulhouseFrance

Personalised recommendations