Terrestrial Laser Scanning (TLS) technology is increasingly used for the generation of accurate 3D models of objects and scenes. But, converting the acquired 3D point cloud data into a representative, semantic 3D model of the scene requires advanced processing and skills. This research field is challenging, particularly when considering inhabited, furnished environments that are characterised by clutter and occlusions. This paper presents a TLS data processing pipeline aimed at producing semantic 3D models of furnished office and home interiors. The structure of rooms (floor, ceiling, and walls with window and door openings) is created using Boundary Representation (B-Rep) models, that not only encode the geometry of those elements, but also their connectivity. Windows and doors are recognized and modelled using a novel method based on molding detection. For the furniture, the approach uniquely integrates smart technology (RFID) that is increasingly used for Facilities Management (FM). RFID tags attached to furniture as sensed at the same time as laser scanning is conducted. The collected IDs are used to retrieve discriminatory geometric information about those objects from the building’s FM database, that this information is used to support their recognition and modeling in the point cloud data. The manuscript particularly reports results for the recognition and modeling of chairs, tables and wardrobes (and other similar objects like chest of drawers). Extended experimentation of the method has been carried out in real scenarios yielding encouraging results.
|Journal||Journal of Computing in Civil Engineering|
|Early online date||7 Sep 2015|
|Publication status||Published - Jul 2016|