@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}
}
@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}
}
@article{DischerRichterDoellner2016,
  author    = {Discher, S{\"o}ren and Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Interactive and View-Dependent See-Through Lenses for Massive 3D Point Clouds},
  series = {Advances in 3D Geoinformation},
  journal   = {Advances in 3D Geoinformation},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-319-25691-7},
  issn      = {1863-2246},
  doi       = {10.1007/978-3-319-25691-7_3},
  pages     = {49 -- 62},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}