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SIGGRAPH 2014 Preview -"Shape Collection" Session

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Slide for SIGGRAPH2014 seminar @ui-lab, UTokyo. …

Slide for SIGGRAPH2014 seminar @ui-lab, UTokyo.

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  • Exact subgraph isomoraphismでざっくりとfocal points候補を探してから、
    多少の編集距離を許容したinexact subgraph matchingで似ているけどグラフ同型でないものもサポーターに加える
    逆にグラフは同型だけど実際の配置がかなり異なるものを除去して、クリーンアップ
    最後にthresholdでカットオフしてfocal pointsを決める
  • Exact subgraph isomoraphismでざっくりとfocal points候補を探してから、
    多少の編集距離を許容したinexact subgraph matchingで似ているけどグラフ同型でないものもサポーターに加える
    逆にグラフは同型だけど実際の配置がかなり異なるものを除去して、クリーンアップ
    最後にthresholdでカットオフしてfocal pointsを決める
  • Assumption: 同じ機能を果たすパーツは同じ形をしていなくても同じような相対的配置をしているはず
  • Transcript

    • 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

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