@misc{StojanovicTrappRichteretal.2018,
  author    = {Stojanovic, Vladeta and Trapp, Matthias and Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {A service-oriented approach for classifying 3D points clouds by example of office furniture classification},
  series = {Web3D 2018: Proceedings of the 23rd International ACM Conference on 3D Web Technology},
  journal   = {Web3D 2018: Proceedings of the 23rd International ACM Conference on 3D Web Technology},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  isbn      = {978-1-4503-5800-2},
  doi       = {10.1145/3208806.3208810},
  pages     = {1 -- 9},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@misc{DischerRichterDoellner2018,
  author    = {Discher, S{\"o}ren and Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {A scalable webGL-based approach for visualizing massive 3D point clouds using semantics-dependent rendering techniques},
  series = {Web3D 2018: The 23rd International ACM Conference on 3D Web Technology},
  journal   = {Web3D 2018: The 23rd International ACM Conference on 3D Web Technology},
  editor    = {Spencer, SN},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  isbn      = {978-1-4503-5800-2},
  doi       = {10.1145/3208806.3208816},
  pages     = {1 -- 9},
  year      = {2018},
  abstract  = {3D point cloud technology facilitates the automated and highly detailed digital acquisition of real-world environments such as assets, sites, cities, and countries; the acquired 3D point clouds represent an essential category of geodata used in a variety of geoinformation applications and systems. In this paper, we present a web-based system for the interactive and collaborative exploration and inspection of arbitrary large 3D point clouds. Our approach is based on standard WebGL on the client side and is able to render 3D point clouds with billions of points. It uses spatial data structures and level-of-detail representations to manage the 3D point cloud data and to deploy out-of-core and web-based rendering concepts. By providing functionality for both, thin-client and thick-client applications, the system scales for client devices that are vastly different in computing capabilities. Different 3D point-based rendering techniques and post-processing effects are provided to enable task-specific and data-specific filtering and highlighting, e.g., based on per-point surface categories or temporal information. A set of interaction techniques allows users to collaboratively work with the data, e.g., by measuring distances and areas, by annotating, or by selecting and extracting data subsets. Additional value is provided by the system's ability to display additional, context-providing geodata alongside 3D point clouds and to integrate task-specific processing and analysis operations. We have evaluated the presented techniques and the prototype system with different data sets from aerial, mobile, and terrestrial acquisition campaigns with up to 120 billion points to show their practicality and feasibility.},
  language  = {en}
}