A service-oriented approach for classifying 3D points clouds by example of office furniture classification
- The rapid digitalization of the Facility Management (FM) sector has increased the demand for mobile, interactive analytics approaches concerning the operational state of a building. These approaches provide the key to increasing stakeholder engagement associated with Operation and Maintenance (O&M) procedures of living and working areas, buildings, and other built environment spaces. We present a generic and fast approach to process and analyze given 3D point clouds of typical indoor office spaces to create corresponding up-to-date approximations of classified segments and object-based 3D models that can be used to analyze, record and highlight changes of spatial configurations. The approach is based on machine-learning methods used to classify the scanned 3D point cloud data using 2D images. This approach can be used to primarily track changes of objects over time for comparison, allowing for routine classification, and presentation of results used for decision making. We specifically focus on classification, segmentation, andThe rapid digitalization of the Facility Management (FM) sector has increased the demand for mobile, interactive analytics approaches concerning the operational state of a building. These approaches provide the key to increasing stakeholder engagement associated with Operation and Maintenance (O&M) procedures of living and working areas, buildings, and other built environment spaces. We present a generic and fast approach to process and analyze given 3D point clouds of typical indoor office spaces to create corresponding up-to-date approximations of classified segments and object-based 3D models that can be used to analyze, record and highlight changes of spatial configurations. The approach is based on machine-learning methods used to classify the scanned 3D point cloud data using 2D images. This approach can be used to primarily track changes of objects over time for comparison, allowing for routine classification, and presentation of results used for decision making. We specifically focus on classification, segmentation, and reconstruction of multiple different object types in a 3D point-cloud scene. We present our current research and describe the implementation of these technologies as a web-based application using a services-oriented methodology.…
Author details: | Vladeta StojanovicORCiDGND, Matthias TrappORCiDGND, Rico RichterORCiDGND, Jürgen Roland Friedrich DöllnerORCiDGND |
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DOI: | https://doi.org/10.1145/3208806.3208810 |
ISBN: | 978-1-4503-5800-2 |
Title of parent work (English): | Web3D 2018: Proceedings of the 23rd International ACM Conference on 3D Web Technology |
Publisher: | Association for Computing Machinery |
Place of publishing: | New York |
Publication type: | Other |
Language: | English |
Year of first publication: | 2018 |
Publication year: | 2018 |
Release date: | 2022/02/21 |
Tag: | 3D Point Clouds; BIM; Indoor Models; Learning; Machine; Service-Oriented |
Number of pages: | 9 |
First page: | 1 |
Last Page: | 9 |
Funding institution: | Research School on Service-Oriented Systems Engineering of the Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam |
Organizational units: | An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
Peer review: | Referiert |