Least squares conformal maps for automatic texture atlas generation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Deformation transfer for triangle meshes
ACM SIGGRAPH 2004 Papers
Recovering articulated object models from 3D range data
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
ACM SIGGRAPH 2005 Papers
Mesh Segmentation - A Comparative Study
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Laplace-Beltrami Eigenfunctions Towards an Algorithm That "Understands" Geometry
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Example-based skeleton extraction
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Laplace-Beltrami eigenfunctions for deformation invariant shape representation
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Articulated mesh animation from multi-view silhouettes
ACM SIGGRAPH 2008 papers
A concise and provably informative multi-scale signature based on heat diffusion
SGP '09 Proceedings of the Symposium on Geometry Processing
International Journal of Computer Vision
Contextual Part Analogies in 3D Objects
International Journal of Computer Vision
Learning shape segmentation using constrained spectral clustering and probabilistic label transfer
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Visual vocabulary signature for 3D object retrieval and partial matching
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
SHREC'10 track: non-rigid 3D shape retrieval
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Shape analysis using the edge-based laplacian
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Key-components: detection of salient regions on 3D meshes
The Visual Computer: International Journal of Computer Graphics
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We propose a novel method for pose-consistent segmentation of non-rigid 3D shapes into visually meaningful parts. The key idea is to study the shape in the framework of quantum mechanics and to group points on the surface which have similar probability of presence for quantum mechanical particles. For each point on an object's surface these probabilities are encoded by a feature vector, the Wave Kernel Signature (WKS). Mathematically, the WKS is an expression in the eigenfunctions of the Laplace-Beltrami operator of the surface. It characterizes the relation of surface points to the remaining surface at various spatial scales. Gaussian mixture clustering in the feature space spanned by the WKS signature for shapes in several poses leads to a grouping of surface points into different and meaningful segments. This enables us to perform consistent and robust segmentation of new versions of the shape. Experimental results demonstrate that the detected subdivision agrees with the human notion of shape decomposition (separating hands, arms, legs and head from the torso for example). We show that the method is robust to data perturbed by various kinds of noise. Finally we illustrate the usefulness of a pose-consistent segmentation for the purpose of shape retrieval.