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 - JOUR A1 - Richter, Rico A1 - Döllner, Jürgen Roland Friedrich T1 - Concepts and techniques for integration, analysis and visualization of massive 3D point clouds JF - Computers, environment and urban systems N2 - Remote sensing methods, such as LiDAR and image-based photogrammetry, are established approaches for capturing the physical world. Professional and low-cost scanning devices are capable of generating dense 3D point clouds. Typically, these 3D point clouds are preprocessed by GIS and are then used as input data in a variety of applications such as urban planning, environmental monitoring, disaster management, and simulation. The availability of area-wide 3D point clouds will drastically increase in the future due to the availability of novel capturing methods (e.g., driver assistance systems) and low-cost scanning devices. Applications, systems, and workflows will therefore face large collections of redundant, up-to-date 3D point clouds and have to cope with massive amounts of data. Hence, approaches are required that will efficiently integrate, update, manage, analyze, and visualize 3D point clouds. In this paper, we define requirements for a system infrastructure that enables the integration of 3D point clouds from heterogeneous capturing devices and different timestamps. Change detection and update strategies for 3D point clouds are presented that reduce storage requirements and offer new insights for analysis purposes. We also present an approach that attributes 3D point clouds with semantic information (e.g., object class category information), which enables more effective data processing, analysis, and visualization. Out-of-core real-time rendering techniques then allow for an interactive exploration of the entire 3D point cloud and the corresponding analysis results. Web-based visualization services are utilized to make 3D point clouds available to a large community. The proposed concepts and techniques are designed to establish 3D point clouds as base datasets, as well as rendering primitives for analysis and visualization tasks, which allow operations to be performed directly on the point data. Finally, we evaluate the presented system, report on its applications, and discuss further research challenges. KW - 3D point clouds KW - System architecture KW - Classification KW - Out-of-core KW - Visualization Y1 - 2014 U6 - https://doi.org/10.1016/j.compenvurbsys.2013.07.004 SN - 0198-9715 SN - 1873-7587 VL - 45 SP - 114 EP - 124 PB - Elsevier CY - Oxford ER - 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 - Discher, Sören A1 - Richter, Rico A1 - Döllner, Jürgen Roland Friedrich T1 - Interactive and View-Dependent See-Through Lenses for Massive 3D Point Clouds JF - Advances in 3D Geoinformation N2 - 3D point clouds are a digital representation of our world and used in a variety of applications. They are captured with LiDAR or derived by image-matching approaches to get surface information of objects, e.g., indoor scenes, buildings, infrastructures, cities, and landscapes. We present novel interaction and visualization techniques for heterogeneous, time variant, and semantically rich 3D point clouds. Interactive and view-dependent see-through lenses are introduced as exploration tools to enhance recognition of objects, semantics, and temporal changes within 3D point cloud depictions. We also develop filtering and highlighting techniques that are used to dissolve occlusion to give context-specific insights. All techniques can be combined with an out-of-core real-time rendering system for massive 3D point clouds. We have evaluated the presented approach with 3D point clouds from different application domains. The results show the usability and how different visualization and exploration tasks can be improved for a variety of domain-specific applications. KW - 3D point clouds KW - LIDAR KW - Visualization KW - Point-based rendering Y1 - 2016 SN - 978-3-319-25691-7 SN - 978-3-319-25689-4 U6 - https://doi.org/10.1007/978-3-319-25691-7_3 SN - 1863-2246 SP - 49 EP - 62 PB - Springer CY - Cham ER -