@inproceedings{EggertSchadeBroehletal.2024,
  author    = {Eggert, Mathias and Schade, Maximilian and Br{\"o}hl, Florian and Moriz, Alexander},
  title     = {Generating synthetic LiDAR point cloud data for object detection using the Unreal Game Engine},
  series = {Design Science Research for a Resilient Future (DESRIST 2024)},
  booktitle = {Design Science Research for a Resilient Future (DESRIST 2024)},
  editor    = {Mandviwalla, Munir and S{\"o}llner, Matthias and Tuunanen, Tuure},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-61174-2 (Print)},
  doi       = {10.1007/978-3-031-61175-9_20},
  pages     = {295 -- 309},
  year      = {2024},
  abstract  = {Object detection based on artificial intelligence is ubiquitous in today's computer vision research and application. The training of the neural networks for object detection requires large and high-quality datasets. Besides datasets based on image data, datasets derived from point clouds offer several advantages. However, training datasets are sparse and their generation requires a lot of effort, especially in industrial domains. A solution to this issue offers the generation of synthetic point cloud data. Based on the design science research method, the work at hand proposes an approach and its instantiation for generating synthetic point cloud data based on the Unreal Engine. The point cloud quality is evaluated by comparing the synthetic cloud to a real-world point cloud. Within a practical example the applicability of the Unreal Game engine for synthetic point cloud generation could be successfully demonstrated.},
  language  = {de}
}