Planar Shape Detection and Regularization in Tandem

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 bet...

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Vydané v:Computer graphics forum Ročník 35; číslo 1; s. 203 - 215
Hlavní autori: Oesau, Sven, Lafarge, Florent, Alliez, Pierre
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.02.2016
Predmet:
Algorithms
Analysis
and object representations
geometric modeling
I.3.5 [Computational Geometry and Object Modeling]: Curve
I.3.5 [Computational Geometry and Object Modeling]: Curve, surface, solid, and object representations; I.4.8 [Scene Analysis]: Shape, Surface fitting
I.4.8 [Scene Analysis]: Shape
Image processing systems
Modelling
Pipelines
Regularity
Regularization
Reinforcement
Resilience
scanning/acquisition
Seeds
solid
Studies
surface
Surface fitting
ISSN:0167-7055, 1467-8659
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Popis
Shrnutí: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). 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).
Bibliografia:ERC Starting Grant 'Robust Geometry Processing' - No. 257474
ark:/67375/WNG-G6SMH9P6-J
ArticleID:CGF12720
istex:4E161BDD3B42CDB050EC89A8EEED6AE07441AE8D
Airbus-Defense and Space
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12720