An IFC schema extension and binary serialization format to efficiently integrate point cloud data into building models. (August 2017)

• Record Type: Journal Article
• Authors: Krijnen, Thomas; Beetz, Jakob
• Is Part Of: Advanced engineering informatics. Volume 33(2017)
• Page Start: 473
• Page End: 490
• Publication Date: 2017-08
• Subjects: Point clouds -- Industry Foundation Classes -- Building information modeling -- HDF5 -- Binary storage -- Serialization; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2017.03.008
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: In this paper we suggest an extension to the Industry Foundation Classes (IFC) model to integrate point cloud datasets. The proposal includes a schema extension to the core model allowing the storage of points, either as Cartesian coordinates, points in parametric space of associated building element surfaces or as discrete height fields projected as grids onto building elements. To handle the considerable amounts of data generated in the process of scanning building structures, we present intelligent compression approaches combined with the Hierarchical Data Format (HDF) as an efficient serialization and an alternative to clear text encoded ISO 10303 part 21 files. Based on prototypical implementations we show results of various serialization options and their impacts on storage efficiency. In this proposal the deepened semantic relationships have been favoured over compression ratios. Nevertheless, with various near-lossless layers of compression and binary serialization applied, a compression ratio of up to 67.7% is obtained for a building model with integrated point clouds, compared to the raw source data. The binary serialization is able to handle hundreds of millions of points, out of which specific spatial and semantic subsets can rapidly be extracted due to the containerized hierarchical storage model and association to building elements. The authors advocate the use of binary storage for sizeable point cloud scans, but also show how especially the grid discretization can result into usable points cloud segments embedded into text-based IFC models.