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.
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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).
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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
- Feature detection
- Animated mesh
- Multi-scale representation
- Difference of Gaussian