ABSTRACT:The use of Building Information Modeling (BIM) for existing buildings based on point clouds is increasing. Standardized geometric quality assessment of the BIMs is needed to make them more reliable and thus reusable for future users. First, available literature on the subject is studied. Next, an initial proposal for a standardized geometric quality assessment is presented. Finally, this method is tested and evaluated with a case study. The number of specifications on BIM relating to existing buildings is limited. The Levels of Accuracy (LOA) specification of the USIBD provides definitions and suggestions regarding geometric model accuracy, but lacks a standardized assessment method. A deviation analysis is found to be dependent on (1) the used mathematical model, (2) the density of the point clouds and (3) the order of comparison. Results of the analysis can be graphical and numerical. An analysis on macro (building) and micro (BIM object) scale is necessary. On macro scale, the complete model is compared to the original point cloud and vice versa to get an overview of the general model quality. The graphical results show occluded zones and non-modeled objects respectively. Colored point clouds are derived from this analysis and integrated in the BIM. On micro scale, the relevant surface parts are extracted per BIM object and compared to the complete point cloud. Occluded zones are extracted based on a maximum deviation. What remains is classified according to the LOA specification. The numerical results are integrated in the BIM with the use of object parameters.

Point cloud segmentation is a crucial step in scene understanding and interpretation. The goal is to decompose the initial data into sets of workable clusters with similar properties. Additionally, it is a key aspect in the automated procedure from point cloud data to BIM. Current approaches typically only segment a single type of primitive such as planes or cylinders. Also, current algorithms suffer from oversegmenting the data and are often sensor or scene dependent.<br><br> In this work, a method is presented to automatically segment large unstructured point clouds of buildings. More specifically, the segmentation is formulated as a graph optimisation problem. First, the data is oversegmented with a greedy octree-based region growing method. The growing is conditioned on the segmentation of planes as well as smooth surfaces. Next, the candidate clusters are represented by a Conditional Random Field after which the most likely configuration of candidate clusters is computed given a set of local and contextual features. The experiments prove that the used method is a fast and reliable framework for unstructured point cloud segmentation. Processing speeds up to 40,000 points per second are recorded for the region growing. Additionally, the recall and precision of the graph clustering is approximately 80%. Overall, nearly 22% of oversegmentation is reduced by clustering the data. These clusters will be classified and used as a basis for the reconstruction of BIM models.

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Sharing building information over the Web is becoming more popular, leading to advances in describing building models in a Semantic Web context. However, those descriptions lack unified approaches for linking geometry descriptions to building elements, derived properties and derived other geometry descriptions. To bridge this gap, we analyse the basic characteristics of geometric dependencies and propose the Ontology for Managing Geometry (OMG) based on this analysis. In this paper, we present our results and show how the OMG provides means to link geometric and non-geometric data in meaningful ways. Thus, exchanging building data, including geometry, on the Web becomes more efficient.

Building Information Modelling (BIM) has changed the way in which buildings are conceived, planned and executed. Apart from their frequent use for as-planned buildings, BIM authoring tools have now been adopted for a number of years for digitising existing buildings as well, mostly by performing a 'scan-to-BIM' process: the creation of a BIM model, primarily based on point clouds. However, some inherent characteristics of existing buildings are complicating such a process: uncertainties, geometric irregularities, classification of heritage building components, linking sources about the real-world asset and an interdisciplinarity that may go beyond traditional Architecture, Engineering and Construction (AEC) topics (e.g. heritage, Facility Management, sensor data and damage assessment). In this paper, a framework called 'scan-to-graph' (STG) is proposed to integrate the concepts of scan-to-BIM with Semantic Web technologies, as these could provide answers to the above-mentioned challenges, most notably on documentation of uncertainties, sources and modelling decisions, element classification and cross-discipline information linking. In order to test the STG concept, a use case was developed where the Audience Room of the Gravensteen castle in Ghent was reconstructed from point clouds, semantically enriched and stored as an RDF graph. The resulting graph contains multiple interlinked geometry types, metadata about the reconstruction process and the sources and allows to unambiguously refer to other contextual data on the Web.