Abstract
Abstract. 3D models of indoor environments are important in many applications, but they usually exist only for newly constructed buildings. Automated approaches to modelling indoor environments from imagery and/or point clouds can make the process easier, faster and cheaper. We present an approach to 3D indoor modelling based on a shape grammar. We demonstrate that interior spaces can be modelled by iteratively placing, connecting and merging cuboid shapes. We also show that the parameters and sequence of grammar rules can be learned automatically from a point cloud. Experiments with simulated and real point clouds show promising results, and indicate the potential of the method in 3D modelling of large indoor environments.
Highlights
Spatial data of indoor environments, where we spend a considerable amount of our time, are important for a variety of applications
Preliminary experiments were carried out using a simulated point cloud of a two-storey building and a real point cloud acquired by terrestrial laser scanning of a large interior space
A point cloud of an interior space with a long corridor of a building in the University of Vigo was acquired by a terrestrial laser scanner for the second experiment
Summary
Spatial data of indoor environments, where we spend a considerable amount of our time, are important for a variety of applications. For most buildings the available spatial data are either 2D floor plans or design building information models (BIM), which do not represent the current state of the building Many applications such as crisis management, routing and navigation, energy efficiency analysis, structural health monitoring and maintenance planning require up-to-date 3D indoor models with rich semantics. Several methods have been developed to automatically generate indoor models based on imagery and/or point cloud data. In this paper we present a method for the modelling of indoor environments based on a simple shape grammar.
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