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