KAUST DepartmentVisual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
KAUST Grant NumberOCRF-2014-CGR3-62140401
Online Publication Date2017-12-25
Print Publication Date2017-10
Permanent link to this recordhttp://hdl.handle.net/10754/627151
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AbstractWe 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.
CitationNan 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.
SponsorsThis 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 name16th IEEE International Conference on Computer Vision, ICCV 2017
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