Abstract
Laser scanners are widely used for the modelling of existing buildings and particularly in the creation process of as-built BIM (Building Information Modelling). However, the generation of as-built BIM from point clouds involves mainly manual steps and it is consequently time consuming and error-prone. Along the path to automation, a three steps segmentation approach has been developed. This approach is composed of two phases: a segmentation into sub-spaces namely floors and rooms and a plane segmentation combined with the identification of building elements. <br><br> In order to assess and validate the developed approach, different case studies are considered. Indeed, it is essential to apply algorithms to several datasets and not to develop algorithms with a unique dataset which could influence the development with its particularities. Indoor point clouds of different types of buildings will be used as input for the developed algorithms, going from an individual house of almost one hundred square meters to larger buildings of several thousand square meters. Datasets provide various space configurations and present numerous different occluding objects as for example desks, computer equipments, home furnishings and even wine barrels. For each dataset, the results will be illustrated. The analysis of the results will provide an insight into the transferability of the developed approach for the indoor modelling of several types of buildings.
Highlights
The modelling of indoor areas is a huge issue since the emergence of Building Information Modelling (BIM) and its numerous benefits notably for restoration, documentation and maintenance of buildings
The second phase consists in a plane segmentation combined with the identification of building elements
TRANSFERABILITY ANALYSIS 4.1 Datasets used for transferability analysis In order to analyse the transferability of the described approach, three datasets, which were not used for the development of the approach, are considered (Figure 7)
Summary
The modelling of indoor areas is a huge issue since the emergence of Building Information Modelling (BIM) and its numerous benefits notably for restoration, documentation and maintenance of buildings. Lasers scanners are widely used to collect information about the as-is conditions of existing buildings They allow fast acquisitions and provide a very high level of details through point clouds representation. Considering indoor areas, several works deal with sub-space segmentation of point clouds. Instead of a sub-space segmentation, some works directly consider a segmentation into planes of indoor point clouds (Jung et al, 2014, Thomson and Boehm, 2015, Xiong et al, 2013). The approach for indoor building modelling developed by Macher et al (2015) will be considered. It has been slightly modified and completed. After presenting datasets and items for the approach validation, results obtained with several datasets with the described algorithms are presented and analysed by following these validation items
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