Planar Shape Detection and Regularization in Tandem

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dc.contributor.authorOesau, Svenen_US
dc.contributor.authorLafarge, Florenten_US
dc.contributor.authorAlliez, Pierreen_US
dc.contributor.editorChen, Min and Zhang, Hao (Richard)en_US
dc.date.accessioned2016-03-01T14:13:10Z
dc.date.available2016-03-01T14:13:10Z
dc.date.issued2016en_US
dc.description.abstractWe present a method for planar shape detection and regularization from raw point sets. The geometric modelling and processing of man‐made environments from measurement data often relies upon robust detection of planar primitive shapes. In addition, the detection and reinforcement of regularities between planar parts is a means to increase resilience to missing or defect‐laden data as well as to reduce the complexity of models and algorithms down the modelling pipeline. The main novelty behind our method is to perform detection and regularization in tandem. We first sample a sparse set of seeds uniformly on the input point set, and then perform in parallel shape detection through region growing, interleaved with regularization through detection and reinforcement of regular relationships (coplanar, parallel and orthogonal). In addition to addressing the end goal of regularization, such reinforcement also improves data fitting and provides guidance for clustering small parts into larger planar parts. We evaluate our approach against a wide range of inputs and under four criteria: geometric fidelity, coverage, regularity and running times. Our approach compares well with available implementations such as the efficient random sample consensus–based approach proposed by Schnabel and co‐authors in 2007.We present a method for planar shape detection and regularization from raw point sets. The geometric modelling and processing of man‐made environments from measurement data often relies upon robust detection of planar primitive shapes. In addition, the detection and reinforcement of regularities between planar parts is a means to increase resilience to missing or defect‐laden data as well as to reduce the complexity of models and algorithms down the modelling pipeline. The main novelty behind our method is to perform detection and regularization in tandem. We first sample a sparse set of seeds uniformly on the input point set, and then perform in parallel shape detection through region growing, interleaved with regularization through detection and reinforcement of regular relationships (coplanar, parallel and orthogonal).en_US
dc.description.number1en_US
dc.description.sectionheadersArticlesen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume35en_US
dc.identifier.doi10.1111/cgf.12720en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12720en_US
dc.publisherCopyright © 2016 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectgeometric modelingen_US
dc.subjectscanning/acquisitionen_US
dc.subjectI.3.5 [Computational Geometry and Object Modeling]: Curve, surface, solid, and object representations; I.4.8 [Scene Analysis]: Shape, Surface fittingen_US
dc.titlePlanar Shape Detection and Regularization in Tandemen_US
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