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2017.ICCV.Liangliang.PolyFit.pdf
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Conference PaperAuthors
Nan, Liangliang
Wonka, Peter

KAUST Department
Visual Computing Center (VCC)Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
KAUST Grant Number
OCRF-2014-CGR3-62140401Date
2017-12-25Online Publication Date
2017-12-25Print Publication Date
2017-10Permanent link to this record
http://hdl.handle.net/10754/627151
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Show full item recordAbstract
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.Conference/Event name
16th IEEE International Conference on Computer Vision, ICCV 2017Embedded External Content
ae974a485f413a2113503eed53cd6c53
10.1109/ICCV.2017.258