Technical Papers

Understanding Shape

Wednesday, 28 July | 3:45 PM - 5:15 PM | Room 502 B
Session Chair: Misha Kazhdan, John Hopkins University
Discrete-Scale Axis Representations for 3D Geometry

This paper addresses the fundamental problem of computing stable medial representations of 3D shapes. A spatially adaptive classification of features yields a robust algorithm for generating medial representations at different levels of abstraction. The method demonstrates application of the algorithm to hundreds of shapes, including complex geometries consisting of millions of triangles.

Balint Miklos
ETH Zürich

Joachim Giesen
Friedrich-Schiller-Universität Jena

Mark Pauly
Ecole Polytechnique Federale de Lausanne

Learning 3D Mesh Segmentation and Labeling

This paper presents a data-driven approach to simultaneous segmentation and labeling of parts in 3D meshes. The algorithm uses hundreds of geometric and contextual features, and learns different types of segmentations for different tasks, without requiring manual parameter tuning.

Evangelos Kalogerakis
University of Toronto

Aaron Hertzmann
University of Toronto

Karan Singh
University of Toronto

Symmetry-Factored Embedding and Distance

This paper introduces symmetry-factored embedding (SFE) and the corresponding symmetry-factored distance (SFD) as new tools to detect, analyze, and quantify symmetry in point sets.

Yaron Lipman
Princeton University

Xiaobai Chen
Princeton University

Ingrid Daubechies
Princeton University

Thomas Funkhouser
Princeton University

A Connection Between Partial Symmetry and Inverse Procedural Modeling

This framework for computing shape grammars from 3D example geometry encodes objects that are locally similar to the exemplar, which is used for semi-automatic modeling. This approach is the first inverse procedural-modeling technique for 3D shapes that computes shape grammars without user interaction.

Martin Bokeloh
Max-Planck-Institut für Informatik

Michael Wand
Universität des Saarlandes and Max-Planck-Institut für Informatik

Hans-Peter Seidel
Max-Planck-Institut für Informatik

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