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Planar Shape Detection at Structural Scales

Fang, Hao and Lafarge, Florent and Desbrun, Mathieu (2018) Planar Shape Detection at Structural Scales. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE , Piscataway, NJ, pp. 2965-2973. ISBN 978-1-5386-6420-9.

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Interpreting 3D data such as point clouds or surface meshes depends heavily on the scale of observation. Yet, existing algorithms for shape detection rely on trial-and-error parameter tunings to output configurations representative of a structural scale. We present a framework to automatically extract a set of representations that capture the shape and structure of man-made objects at different key Abstraction levels. A shape-collapsing process first generates a fine-to-coarse sequence of shape representations by exploiting local planarity. This sequence is then analyzed to identify significant geometric variations between successive representations through a supervised energy minimization. Our framework is flexible enough to learn how to detect both existing structural formalisms such as the CityGML Levels Of Details, and expert-specified levels of Abstraction. Experiments on different input data and classes of man-made objects, as well as comparisons with existing shape detection methods, illustrate the strengths of our approach in terms of efficiency and flexibility.

Item Type:Book Section
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Desbrun, Mathieu0000-0003-3424-6079
Additional Information:© 2018 IEEE. This work has been funded by CSTB. Mathieu Desbrun gratefully acknowledges the Inria International Chair program and the entire Titane team.
Funding AgencyGrant Number
Centre Scientifique et Technique du Bâtiment (CSTB)UNSPECIFIED
Record Number:CaltechAUTHORS:20190222-102622852
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Official Citation:H. Fang, F. Lafarge and M. Desbrun, "Planar Shape Detection at Structural Scales," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018, pp. 2965-2973. doi: 10.1109/CVPR.2018.00313
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93182
Deposited By: Tony Diaz
Deposited On:25 Feb 2019 17:02
Last Modified:03 Mar 2020 13:01

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