[ SIGGRAPH 2011+Technical Papers ] Geometry Acquisition

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Geometry Acquisition

Tuesday, 9 August 10:45 am - 12:15 pm | East Building, Exhibit Hall A

Session Chair: Paolo Cignoni, ISTI-CNR






GlobFit: Consistently Fitting Primitives by Discovering Global Relations
A method to align locally detected RANSAC primitives using global relations, which are learned and conformed to using a combination of graph reduction and constrained optimization. The paper demonstrates applications on synthetic and scanned data, even under s***ctured noise and anisotropic sampling.
Yangyan Li

Shenzhen Institute of Advanced Technology
Xiaokun Wu

Shenzhen Institute of Advanced Technology and Zhejiang University
Yiorgos Chrysanthou

University of Cy***s
Andrei Sharf

Ben-Gurion University and Shenzhen Institute of Advanced Technology
Daniel Cohen-Or

Tel Aviv University
Niloy J. Mitra

King Abdullah University of Science and Technology




Global Registration of Dynamic Range Scans for Articulated Model Recons***ction
The articulated global registration algorithm presented in this work aligns multiple range scans simultaneously to recons***ct a full poseable 3D model. Solving for surface motion using linear-blend skinning and automatically modeled joints allows users to interactively manipulate the resulting 3D model and create new animations.
Will Chang

University of California, San Diego
Matthias Zwicker

Universität Bern




Texture-Lobes for Tree Modeling
In this lobe-based tree representation for modeling trees, the tree’s foliage is abstracted into compact canonical geometry s***ctures, called lobe-textures. The method introduces techniques to encode a given tree as lobe-based representation and decode the representation and synthesize a fully detailed tree model.
Yotam Livny

Shenzhen Institute of Advanced Technology
Soeren Pirk

Universität Konstanz
Zhanglin Cheng

Shenzhen Institute of Advanced Technology
Feilong Yan

Shenzhen Institute of Advanced Technology
Oliver Deussen

Universität Konstanz
Daniel Cohen-Or

Tel-Aviv University
Baoquan Chen

Shenzhen Institute of Advanced Technology




ℓ1-Sparse Recons***ction of Sharp Point Set Surfaces
With this ℓ1-sparse recons***ction of piecewise smooth objects, common objects can be characterized by a small number of features that introduce sparsity into the model. The recons***ction gives rise to shapes that consist mainly of smooth modes, with the objective function residual concentrated near sharp features.
Haim Avron

Tel-Aviv University, IBM T.J. Watson Research Center
Andrei Sharf

Ben Gurion University
Chen Greif

The University of British Columbia
Daniel Cohen-Or

Tel-Aviv University

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