shape analysis and deformation igarashi lab m2 akira ohgawara
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Shape Analysis and Deformation
Igarashi LabM2 Akira Ohgawara
Joint Shape Segmentation with Linear Programming
• Segment the shapes jointly utilizing features from multiple shapes
• Evaluation– Rand index measure
Qixing Huang, Vladlen Koltun, Leonidas GuibasStanford University
• Initial segments• Pairwise joint segmentation– Integer quadratic program– Linear programming relaxation
• Multiway joint segmentation– Linear programming
Shape Space Exploration of Constrained Meshes
• Planar quad (PQ) mesh• Circular mesh• Non-linear constraints
Yong-Liang Yang, Yi-Jun Yang, Helmut Pottmann, Niloy J. MitraKAUST, TU Vienna
Pattern-Aware Shape Deformation Using Sliding Dockers
• Continuous and discrete regular pattern• A discrete algorithm
– adaptively inserts or removes repeated elements in regular patterns to minimize distortion
• Deformation model– Elastic deformation– Structure aware deformation
Martin Bokeloh, Michael Wand, Vladlen Koltun, Hans-Peter SeidelMPI Informatik, Saarland University, and Stanford University
Unsupervised Co-Segmentation of a Set of Shapes via Descriptor-Space Spectral Clustering
Oana Sidi, Oliver van Kaick, Yanir Kleiman, Hao Zhang, Daniel Cohen-OrTel-Aviv University, Simon Fraser University
• Unsupervised co-segmentation– No labeled data
• Comparison to a supervised approach– [Golovinskiy and Funkhouser 2009]
• Per-object segmentation– Mean-shift algorithm [Comaniciu and Meer 2002]
• Diffusion maps– Dissimilarity
– Affinity matrix
• Clustering– An agglomerative hierarchical algorithm
• Statistical model– EM algorithm and the Bayes’ theorem
• Result– Final co-segmentation
• Number of models– From 12 to 44
• Accuracy– From 84.4 to 98.2
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