An IFC schema extension and binary serialization format to efficiently integrate point cloud data into building models

Citation data:

Advanced Engineering Informatics, ISSN: 1474-0346, Vol: 33, Page: 473-490

Publication Year:
2017
Usage 34
Abstract Views 30
Clicks 3
Link-outs 1
Captures 41
Readers 41
Social Media 19
Tweets 19
Citations 3
Citation Indexes 3
DOI:
10.1016/j.aei.2017.03.008
Author(s):
Thomas Krijnen; Jakob Beetz
Publisher(s):
Elsevier BV
Tags:
Computer Science
Most Recent Tweet View All Tweets
article description
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.