Semantizing Complex 3D Scenes using Constrained Attribute Grammars

We propose a new approach to automatically semantize complex objects in a 3D scene. For this, we define an expressive formalism combining the power of both attribute grammars and constraint. It offers a practical conceptual interface, which is crucial to write large maintainable specifications. As r...

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Bibliographic Details
Published in:Computer graphics forum Vol. 32; no. 5; pp. 33 - 42
Main Authors: Boulch, A., Houllier, S., Marlet, R., Tournaire, O.
Format: Journal Article
Language:English
Published: Oxford, UK Blackwell Publishing Ltd 01.08.2013
Wiley
Subjects:
3-D graphics
Artificial Intelligence
Automation
Collection
Computer aided design
Computer graphics
Computer Science
Computer Vision and Pattern Recognition
Forests
Formalism
Grammars
I.2.10 [Artificial Intelligence]: Vision and Scene Understanding-D/stereo scene analysis
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Object hierarchies
I.4.8 [Image Processing and Computer Vision]: Scene Analysis-Object recognition
I.5.4 [Pattern Recognition]: Applications-Computer vision
Lidar
Specifications
Studies
Three dimensional
United States > US
constrained attribute grammars
building information model (BIM)
LIDAR
semantics
laser scan
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:We propose a new approach to automatically semantize complex objects in a 3D scene. For this, we define an expressive formalism combining the power of both attribute grammars and constraint. It offers a practical conceptual interface, which is crucial to write large maintainable specifications. As recursion is inadequate to express large collections of items, we introduce maximal operators, that are essential to reduce the parsing search space. Given a grammar in this formalism and a 3D scene, we show how to automatically compute a shared parse forest of all interpretations — in practice, only a few, thanks to relevant constraints. We evaluate this technique for building model semantization using CAD model examples as well as photogrammetric and simulated LiDAR data.
Bibliography:ArticleID:CGF12170
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12170