A service-oriented approach for classifying 3D points clouds by example of office furniture classification
| Autor: | Vladeta Stojanovic, Rico Richter, Matthias Trapp, Jürgen Döllner |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
Process (engineering) business.industry 0211 other engineering and technologies Point cloud 020207 software engineering 02 engineering and technology Object (computer science) computer.software_genre Facility management Analytics 021105 building & construction 0202 electrical engineering electronic engineering information engineering Object type Segmentation Data mining business computer Built environment |
| Zdroj: | Web3D |
| DOI: | 10.1145/3208806.3208810 |
| Popis: | 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, 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. |
| Databáze: | OpenAIRE |
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