Technical Papers
Shape Analysis
Tuesday, 23 July 2:00 PM - 3:30 PM
Session Chair: Misha Kazhdan, The Johns Hopkins University
Co-Hierarchical Analysis of Shape Structures
An unsupervised co-hierarchical analysis of a set of shapes, aimed at discovering their hierarchical part structures and revealing relations between geometrically dissimilar yet functionally equivalent shape parts across the set.
Oliver van Kaick
Simon Fraser University
Kai Xu
National University of Defense Technology
Hao Zhang
Simon Fraser University
Yanzhen Wang
National University of Defense Technology
Shuyang Sun
Simon Fraser University
Ariel Shamir
The Interdisciplinary Center Herzliya
Daniel Cohen-Or
Tel Aviv University
Learning Part-Based Templates From Large Collections of 3D Shapes
An analysis framework to derive structure from large, unorganized, diverse collections of 3D shapes. The automatic algorithm starts with an initial template model that jointly optimizes for part segmentation, point-to-point surface correspondence, and a compact deformation model to best explain the input model collection.
Vladimir Kim
Princeton University
Wilmot Li
Adobe Systems Incorporated
Niloy Mitra
University College London
Siddhartha Chaudhuri
Princeton University
Stephen DiVerdi
Adobe Systems Incorporated, Google Inc.
Thomas Funkhouser
Princeton University
Qualitative Organization of Collections of Shapes Via Quartet Analysis
This method to organize a heterogeneous collection of 3D shapes for overview and exploration applies analysis that combines several distances together into a qualitative measure. It introduces the concept of degree-of-separation charts from every shape and shows its effectiveness for exploration of interactive shapes.
Shi-Sheng Huang
Tsinghua University
Ariel Shamir
Interdisciplinary Cente Herzliya
Chao-Hui Shen
Tsinghua University
Hao Zhang
Simon Fraser University
Alla Sheffer
The University of British Columbia
Shi-Min Hu
Tsinghua University
Daniel Cohen-Or
Tel Aviv University
Map-Based Exploration of Intrinsic Shape Differences and Variability
A novel formulation of shape differences aimed at providing detailed information about the location and nature of the differences or distortions between the shapes being compared. In this method, the difference operator is much more informative than a scalar similarity score, rendering it useful in applications requiring more refined shape comparisons.
Raif Rustamov
Stanford University
Maks Ovsjanikov
École Polytechnique
Omri Azencot
Technion - Israel Institute of Technology
Mirela Ben-Chen
Technion - Israel Institute of Technology
Frederic Chazal
INRIA Saclay - Île-de-France
Leonidas Guibas
Stanford University