ROOM POINT CLOUDS SEGMENTATION: A NEW APPROACH BASED ON OCCUPANCY AND DENSITY IMAGES
Keywords: scan-to-BIM, as-built BIM, room segmentation, 3D point cloud, indoor, image processing, MLS
Abstract. The majority of buildings are existing and have not been constructed in a BIM process. That is why, the modelling of existing buildings becomes a major issue. Beyond the questions of maintenance or renovation with the promise of reducing the environmental impact, their modelling is of interest for documentation and valorisation. But today, while the acquisition techniques are significantly progressing, with the use of efficient laser scanners, the modelling remains manual and very time consuming. The literature is not empty of proposals to automate the process. Nevertheless, many studies are based on strict architectural hypotheses or restricted to unoccupied buildings free of furniture. This strongly limits their application field. In response to these limitations, this paper presents an innovative method retaining only verticality of walls as assumptions. It is based on occupancy and density image analysis. Tested on a wide variety of buildings, this method is very promising with very few classifications errors. Furthermore, the process is successful with dynamic laser scanning data, in cluttered environments, and applied on buildings with a non-Manhattan-World scheme.