Peer-Reviewed Literature

  • Peters T. und Brenner C. (2020): Conditional Adversarial Networks for Multimodal Photo-Realistic Point Cloud RenderingPFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science
    DOI: 10.1007/s41064-020-00114-z
  • Peters T. und Brenner C. (2018): Conditional adversarial networks for multimodal photorealistic point cloud renderingProceedings of the Spatial Big Data and Machine Learning in GIScience, Workshop at GIScience, S. 48–53
  • Schön S., Brenner C., Alkhatib H., Coenen M., Dbouk H., Garcia Fernandez N., Fischer C., Heipke C., Lohmann K., Neumann I., Nguyen U., Paffenholz J.-A., Peters T., Rottensteiner F., Schachtschneider J., Sester M., Sun L., Vogel S., Voges R., Wagner B. (2018): Integrity and Collaboration in Dynamic Sensor NetworksSensors 2018
    DOI: 10.3390/s18072400

Non Peer-Reviewed Literature

  • Koetsier C., Peters T. und Sester M. (2020): Learning the 3D pose of vehicles from 2D vehicle patchesInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Bd. 43. International Society for Photogrammetry und Remote Sensing, (accepted, to appear)
  • Peters T., Brenner C. (2019): Automatic Generation of Large Point Cloud Training Datasets Using Label Transfer39. Wissenschaftlich-Technische Jahrestagung der DGPF e.V., 20. – 22. Februar 2019 in Wien, Thomas P. Kersten (Hrsg.)
    ISSN: 0942-2870