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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. https://resolver.caltech.edu/CaltechAUTHORS:20190222-102622852

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Abstract

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
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/CVPR.2018.00313DOIArticle
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.
Funders:
Funding AgencyGrant Number
Centre Scientifique et Technique du Bâtiment (CSTB)UNSPECIFIED
Record Number:CaltechAUTHORS:20190222-102622852
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190222-102622852
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
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:25 Feb 2019 17:02
Last Modified:03 Oct 2019 20:51

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