@article{StojanovicTrappRichteretal.2019,
  author    = {Stojanovic, Vladeta and Trapp, Matthias and Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Service-oriented semantic enrichment of indoor point clouds using octree-based multiview classification},
  series = {Graphical Models},
  volume    = {105},
  journal   = {Graphical Models},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {1524-0703},
  doi       = {10.1016/j.gmod.2019.101039},
  pages     = {18},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{DischerRichterDoellner2019,
  author    = {Discher, S{\"o}ren and Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Concepts and techniques for web-based visualization and processing of massive 3D point clouds with semantics},
  series = {Graphical Models},
  volume    = {104},
  journal   = {Graphical Models},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {1524-0703},
  doi       = {10.1016/j.gmod.2019.101036},
  pages     = {11},
  year      = {2019},
  abstract  = {3D point cloud technology facilitates the automated and highly detailed acquisition of real-world environments such as assets, sites, and countries. We present a web-based system for the interactive exploration and inspection of arbitrary large 3D point clouds. Our approach is able to render 3D point clouds with billions of points using spatial data structures and level-of-detail representations. Point-based rendering techniques and post-processing effects are provided to enable task-specific and data-specific filtering, e.g., based on semantics. A set of interaction techniques allows users to collaboratively work with the data (e.g., measuring distances and annotating). Additional value is provided by the system's ability to display additional, context-providing geodata alongside 3D point clouds and to integrate processing and analysis operations. We have evaluated the presented techniques and in case studies and with different data sets from aerial, mobile, and terrestrial acquisition with up to 120 billion points to show their practicality and feasibility.},
  language  = {en}
}