Semantizing Complex 3D Scenes using Constrained Attribute Grammars

Alexandre Boulch (1, 2) , Simon Houllier (1, 2) , Renaud Marlet (1, 2, 3) , Olivier Tournaire (1, 4)

1 imagine [Marne-la-Vallée]
2 LIGM - Laboratoire d'Informatique Gaspard-Monge
3 ENPC - École des Ponts ParisTech
4 CSTB - Centre Scientifique et Technique du Bâtiment

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.

Keywords: constrained attribute grammars; LIDAR; laser scan; semantics; building information model (BIM)

Domains: Computer Vision and Pattern Recognition [cs.CV]; Artificial Intelligence [cs.AI]

Alexandre Boulch, Simon Houllier, Renaud Marlet, Olivier Tournaire. Semantizing Complex 3D Scenes using Constrained Attribute Grammars. Computer Graphics Forum, 2013, 32 (5), pp.33-42. ⟨10.1111/cgf.12170⟩. ⟨hal-00864707⟩