TY - JOUR A1 - Isailović, Dušan A1 - Stojanovic, Vladeta A1 - Trapp, Matthias A1 - Richter, Rico A1 - Hajdin, Rade A1 - Döllner, Jürgen Roland Friedrich T1 - Bridge damage BT - detection, IFC-based semantic enrichment and visualization JF - Automation in construction : an international research journal N2 - Building Information Modeling (BIM) representations of bridges enriched by inspection data will add tremendous value to future Bridge Management Systems (BMSs). This paper presents an approach for point cloud-based detection of spalling damage, as well as integrating damage components into a BIM via semantic enrichment of an as-built Industry Foundation Classes (IFC) model. An approach for generating the as-built BIM, geometric reconstruction of detected damage point clusters and semantic-enrichment of the corresponding IFC model is presented. Multiview-classification is used and evaluated for the detection of spalling damage features. The semantic enrichment of as-built IFC models is based on injecting classified and reconstructed damage clusters back into the as-built IFC, thus generating an accurate as-is IFC model compliant to the BMS inspection requirements. KW - damage detection KW - building information modeling KW - 3D point clouds KW - multiview classification KW - bridge management systems Y1 - 2020 U6 - https://doi.org/10.1016/j.autcon.2020.103088 SN - 0926-5805 SN - 1872-7891 VL - 112 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Stojanovic, Vladeta A1 - Trapp, Matthias A1 - Richter, Rico A1 - Döllner, Jürgen Roland Friedrich T1 - Service-oriented semantic enrichment of indoor point clouds using octree-based multiview classification JF - Graphical Models N2 - The use of Building Information Modeling (BIM) for Facility Management (FM) in the Operation and Maintenance (O&M) stages of the building life-cycle is intended to bridge the gap between operations and digital data, but lacks the functionality of assessing the state of the built environment due to non-automated generation of associated semantics. 3D point clouds can be used to capture the physical state of the built environment, but also lack these associated semantics. A prototypical implementation of a service-oriented architecture for classification of indoor point cloud scenes of office environments is presented, using multiview classification. The multiview classification approach is tested using a retrained Convolutional Neural Network (CNN) model - Inception V3. The presented approach for classifying common office furniture objects (chairs, sofas and desks), contained in 3D point cloud scans, is tested and evaluated. The results show that the presented approach can classify common office furniture up to an acceptable degree of accuracy, and is suitable for quick and robust semantics approximation - based on RGB (red, green and blue color channel) cubemap images of the octree partitioned areas of the 3D point cloud scan. Additional methods for web-based 3D visualization, editing and annotation of point clouds are also discussed. Using the described approach, captured scans of indoor environments can be semantically enriched using object annotations derived from multiview classification results. Furthermore, the presented approach is suited for semantic enrichment of lower resolution indoor point clouds acquired using commodity mobile devices. KW - Semantic enrichment KW - 3D point clouds KW - Multiview classification KW - Service-oriented KW - Indoor environments Y1 - 2019 U6 - https://doi.org/10.1016/j.gmod.2019.101039 SN - 1524-0703 SN - 1524-0711 VL - 105 PB - Elsevier CY - San Diego ER - TY - GEN A1 - Stojanovic, Vladeta A1 - Trapp, Matthias A1 - Richter, Rico A1 - Döllner, Jürgen Roland Friedrich T1 - A service-oriented approach for classifying 3D points clouds by example of office furniture classification T2 - Web3D 2018: Proceedings of the 23rd International ACM Conference on 3D Web Technology N2 - 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. KW - Indoor Models KW - 3D Point Clouds KW - Machine KW - Learning KW - BIM KW - Service-Oriented Y1 - 2018 SN - 978-1-4503-5800-2 U6 - https://doi.org/10.1145/3208806.3208810 SP - 1 EP - 9 PB - Association for Computing Machinery CY - New York ER -