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    PolyFit: Polygonal Surface Reconstruction from Point Clouds

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    2017.ICCV.Liangliang.PolyFit.pdf
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    Description:
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    Type
    Conference Paper
    Authors
    Nan, Liangliang cc
    Wonka, Peter cc
    KAUST Department
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    KAUST Grant Number
    OCRF-2014-CGR3-62140401
    Date
    2017-12-25
    Online Publication Date
    2017-12-25
    Print Publication Date
    2017-10
    Permanent link to this record
    http://hdl.handle.net/10754/627151
    
    Metadata
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    Abstract
    We propose a novel framework for reconstructing lightweight polygonal surfaces from point clouds. Unlike traditional methods that focus on either extracting good geometric primitives or obtaining proper arrangements of primitives, the emphasis of this work lies in intersecting the primitives (planes only) and seeking for an appropriate combination of them to obtain a manifold polygonal surface model without boundary.,We show that reconstruction from point clouds can be cast as a binary labeling problem. Our method is based on a hypothesizing and selection strategy. We first generate a reasonably large set of face candidates by intersecting the extracted planar primitives. Then an optimal subset of the candidate faces is selected through optimization. Our optimization is based on a binary linear programming formulation under hard constraints that enforce the final polygonal surface model to be manifold and watertight. Experiments on point clouds from various sources demonstrate that our method can generate lightweight polygonal surface models of arbitrary piecewise planar objects. Besides, our method is capable of recovering sharp features and is robust to noise, outliers, and missing data.
    Citation
    Nan L, Wonka P (2017) PolyFit: Polygonal Surface Reconstruction from Point Clouds. 2017 IEEE International Conference on Computer Vision (ICCV). Available: http://dx.doi.org/10.1109/ICCV.2017.258.
    Sponsors
    This research was supported by the KAUST Office of Sponsored Research (award No. OCRF-2014-CGR3-62140401) and the Visual Computing Center (VCC) at KAUST.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2017 IEEE International Conference on Computer Vision (ICCV)
    Conference/Event name
    16th IEEE International Conference on Computer Vision, ICCV 2017
    DOI
    10.1109/ICCV.2017.258
    Additional Links
    http://ieeexplore.ieee.org/document/8237520/
    https://youtu.be/18Lp5pqlJSc
    Embedded External Content
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICCV.2017.258
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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