The 4th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia
Conference 12-15 December • Exhibition 13-15 December • Hong Kong Convention & Exhibition Centre
 

Technical Papers

Shape Analysis and Deformation

Tuesday, 13 December 09:00 - 10:45 |  Convention Hall B

Session Chair
Marc Alexa

Pattern-Aware Shape Deformation Using Sliding Dockers - Picture

Pattern-Aware Shape Deformation Using Sliding Dockers


We present a structure-aware shape editing technique that preserves regular patterns in the input. The user can apply a free-form deformation and the algorithm automatically inserts or removes repeated elements to maintain the shape structure. Continuous pattern preservation constraints are used to maintain symmetry of regular elements.


Martin Bokeloh, Max-Planck-Institut für Informatik
Michael Wand, Max-Planck-Institut für Informatik
Vladlen Koltun, Stanford University
Hans-Peter Seidel, Max-Planck-Institut für Informatik


Shape Space Exploration of Constrained Meshes - Picture

Shape Space Exploration of Constrained Meshes


We present a general computational framework to locally characterize any shape space implicitly prescribed by a collection of non-linear constraints and navigation of desirable subspaces.


Yong-Liang Yang, King Abdullah University
Yi-Jun Yang, King Abdullah University
Helmut Pottmann, King Abdullah University
Niloy J. Mitra, King Abdullah University of Science and The Vienna University of Technology


Joint Shape Segmentation with Linear Programming - Picture

Joint Shape Segmentation with Linear Programming


We present an approach to segmenting shapes in a heterogenous shape database. Our approach segments the shapes jointly, utilizing features from multiple shapes to improve the segmentation of each. The approach is entirely unsupervised and is based on an integer quadratic programming formulation of the joint segmentation problem.


Qi-xing Huang, Stanford University
Vladlen Koltun, Stanford Unversity
Leonidas Guibas, Stanford Unversity


Unsupervised Co-Segmentation of a Set of Shapes via Descriptor-Space Spectral Clustering - Picture

Unsupervised Co-Segmentation of a Set of Shapes via Descriptor-Space Spectral Clustering


Can we infer more knowledge from a set of shapes rather than from a pair of shapes alone? Our answer is affirmative, and we introduce an algorithm for unsupervised co-segmentation of a set, where the semantic parts of shapes are revealed and their correspondence is established across the set.


Oana Sidi, Tel Aviv University
Oliver van Kaick, Simon Fraser University
Yanir Kleiman, Tel Aviv University
Hao Richard Zhang, Simon Fraser University
Daniel Cohen-Or, Tel Aviv University