1. SIGGRAPH Seminar 2014 Session: Shape Collection Ryohei Suzuki (@quolc, Igarashi Lab M1)

2. Shape Collection 1. Meta-representations of Shape Families 2. Organizing Heterogeneous Scene Collections through Contextual Focal Points 3. Functional Map Networks for Analyzing and Browsing Large Shape Collections (unavailable) 4. Geometry and Context for Semantic Correspondences and Functionality Recognition in Man-made 3D Shapes 5. Learning 3D Attributes of Images through Shape Collection (unavailable) Session: Shape Collection 3. Meta-representations of Shape Families Nar Fish1, Melinos Averkious2, Oliver van Kaick1, Olga Sorkine-Hornung3, Daniel Cohen-Or1, Niloy Mitra2 1Tel Aviv University 2University College London 3ETH Zurich Session: Shape Collection 4. Analyzing co-segmented 3D shape family by relative configurations of segments Probability distribution of relations = identity of family Session: Shape Collection Higher probability more valid shape Meta-representations of Shape Families N. Fish, M. Averkiou, O. van Kaick, O. Sorkine-Hournung, D. Cohen-Or, N. J. Mitra 5. 1. Abstracting shapes by pre-defined segments 2. Analyzing relations between segments Session: Shape Collection computing convex hull extracting OBB relations: scale, angle, contact all pairwise combination relative to whole shape Meta-representations of Shape Families N. Fish, M. Averkiou, O. van Kaick, O. Sorkine-Hournung, D. Cohen-Or, N. J. Mitra 6. 3. Computing probability distributions 1D kernel density estimator (KDE) with common Gaussian kernel Session: Shape Collection (bandwidth setting) Meta-representations of Shape Families N. Fish, M. Averkiou, O. van Kaick, O. Sorkine-Hournung, D. Cohen-Or, N. J. Mitra 7. Exploration of shape families Session: Shape Collection Meta-representations of Shape Families N. Fish, M. Averkiou, O. van Kaick, O. Sorkine-Hournung, D. Cohen-Or, N. J. Mitra 8. Session: Shape Collection Meta-representations of Shape Families N. Fish, M. Averkiou, O. van Kaick, O. Sorkine-Hournung, D. Cohen-Or, N. J. Mitra 9. Organizing Heterogeneous Scene Collections through Contextual Focal Points Kai Xu1,3, Rui Ma2, Hao Zhang2, Chenyang Zhu3, Ariel Shamir4, Daniel Cohen-Or5, Hui Huang1 1SIAT 2Simon Fraser University 3National University of Defense Technology 4The Interdisciplinary Center, 5Tel Aviv University Session: Shape Collection 10. Organizing heterogeneous data (indoor scenes) Holistic (singular view) comparison is not meaningful. (e.g. Paris vs New York) Notion of focal points Representative substructures for attention or focus Yielding multiple distance measures depending on FPs Session: Shape Collection Organizing Heterogeneous Scene Collections through Contextual Focal Points K. Xu, R. Ma, H. Zhang, C. Zhu, A. Shamir, D. Cohen-Or, H. Huang 11. Input: heterogeneous collection of 3D indoor scenes with object labels (bed, table, desk, lamp, chair, etc.) Goal: extracting a set of contextual focal points + clustering scenes based on the focals A contextual focal point is Appearing frequently Inducing a compact cluster (coherence) Focal extraction as optimization Session: Shape Collection Organizing Heterogeneous Scene Collections through Contextual Focal Points K. Xu, R. Ma, H. Zhang, C. Zhu, A. Shamir, D. Cohen-Or, H. Huang 12. Iterative co-analysis algorithm Frequent pattern analysis Exact subgraph isomorphism [Yan & Han 2002] Inexact subgraph matching [Riesen et al. 2010] Weighted (Cluster-guided) subgraph matching [Tsuda & Kudo 2006] Focal-induced scene clustering Session: Shape Collection Organizing Heterogeneous Scene Collections through Contextual Focal Points K. Xu, R. Ma, H. Zhang, C. Zhu, A. Shamir, D. Cohen-Or, H. Huang 13. Iterative co-analysis algorithm Frequent pattern analysis Focal-induced scene clustering Clustering on a (BoW feature) Subspace segmentation via quadratic programming [Wang et al. 2011] Session: Shape Collection Organizing Heterogeneous Scene Collections through Contextual Focal Points K. Xu, R. Ma, H. Zhang, C. Zhu, A. Shamir, D. Cohen-Or, H. Huang 14. Session: Shape Collection Organizing Heterogeneous Scene Collections through Contextual Focal Points K. Xu, R. Ma, H. Zhang, C. Zhu, A. Shamir, D. Cohen-Or, H. Huang 15. Geometry and Context for Semantic Correspondences and Functionality Recognition in Manmade 3D Shapes Hamid Laga1, Michela Mortara2, Michela Spagnuolo2 1University of South Australia 2CNR IMATI-Genova Session: Shape Collection 16. Geometry and Context for Semantic Correspondences and Functionality Recognition in Manmade 3D Shapes H. Laga, M. Mortara, M. Spagnuolo Target: recognizing semantic correspondence between parts of man-made 3D shapes Significant intra-class variations in geometry & topology Purely local analysis is useless! Goal: unsupervised solution for this problem Idea: using contextual information (part relations) Graph representation & context-aware subgraph similarity 17. Input: single class 3D shape collection (e.g. vases) Output: segmentation w/ semantic correspondence Algorithm 1. Automatic segmentation of 3D object Any algorithm is OK. 2. Constructing graph representation Node = part, Edge = structural relationship Context of part S = substructure around S 3. Finding correspondence based on similarities on graph Session: Shape Collection Geometry and Context for Semantic Correspondences and Functionality Recognition in Manmade 3D Shapes H. Laga, M. Mortara, M. Spagnuolo 18. Initial graph construction Inter-part symmetries Adjacency Other contextual relation ships (e.g. enclosure, contact, support) Building segmentation hierarchy by clique contraction Geometry and Context for Semantic Correspondences and Functionality Recognition in Manmade 3D Shapes H. Laga, M. Mortara, M. Spagnuolo Edge Merge! Merge! 19. Calculating part-wise correspondence Session: Shape Collection Geometry and Context for Semantic Correspondences and Functionality Recognition in Manmade 3D Shapes H. Laga, M. Mortara, M. Spagnuolo Geometric Similarity Contextual Similarity p-order similarity function between Part PA on Graph G1 & Part PB on Graph G2 Compare subgraphs (nodes) by context-aware graph kernel 20. Correspondence results Session: Shape Collection Geometry and Context for Semantic Correspondences and Functionality Recognition in Manmade 3D Shapes H. Laga, M. Mortara, M. Spagnuolo 21. Functional recognition Using graph kernel for supervised learning (SVM) Training with labeled 3D objects Building binary classifiers Is this part graspable or not? Geometry and Context for Semantic Correspondences and Functionality Recognition in Manmade 3D Shapes H. Laga, M. Mortara, M. Spagnuolo