Torben Peters

Arbeitete als wissenschaftlicher Mitarbeiter im DFG-Graduiertenkollege i.c.sens. Er forschte über die automatische Klassifikation von Punktwolken in semantisch bedeutsame Objekte. Hierzu nutzte er die Farbinformation aus den Bilddaten.

 

 

Publikationen

Begutachtete Zeitschriftenartikel und Buchkapitel

  • Peters, T., Brenner, C., & Schindler, K. (2023): Semantic segmentation of mobile mapping point clouds via multi-view label transferISPRS Journal of Photogrammetry and Remote Sensing, 202, 30-39.
  • Malinovskaya, A., Otto, P., Peters, T. (2022): Statistical learning for change point and anomaly detection in graphs.Artificial Intelligence, Big Data and Data Science in Statistics (pp. 85-109). Springer, Cham.
    DOI: doi.org/10.1007/978-3-031-07155-3_4
    arXiv: 2011.06080
  • Maneejuk, P., Peters, T., Brenner, C., & Kreinovich, V. (2022): How to Train A-to-B and B-to-A Neural Networks So That the Resulting Transformations Are (Almost) Exact InversesIn: Credible Asset Allocation, Optimal Transport Methods, and Related Topics (pp. 203-209). Cham: Springer International Publishing.
  • 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
  • Steffen Schön, Claus Brenner, Hamza Alkhatib, Max Coenen, Hani Dbouk, Nicolas Garcia-Fernandez, Colin Fischer, Christian Heipke, Katja Lohmann, Ingo Neumann, Uyen Nguyen, Jens-André Paffenholz, Torben Peters, Franz Rottensteiner, Julia Schachtschneider, Monika Sester, Ligang Sun, Sören Vogel, Raphael Voges und Bernardo Wagner (2018): Integrity and Collaboration in Dynamic Sensor NetworksSensors (Basel, Switzerland) 18.7.
    DOI: http://www.mdpi.com/1424-8220/18/7/2400

Begutachtete Konferenzbeiträge

  • Peters, T., Schindler, K., & Brenner, C. (2022): Self-Supervised Adversarial Shape CompletionISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 143-150.
    DOI: https://doi.org/10.5194/isprs-annals-V-2-2022-143-2022
  • Peters, T., Brenner, C. (2018): Conditional Adversarial Networks for Multimodal Photo-Realistic Point Cloud RenderingSpatial Big Data and Machine Learning in GIScience, GIScience Workshop 2018 Weitere Informationen

Konferenzbeiträge

  • Koetsier, C., Peters, T., & Sester, M. (2020): Learning the 3D Pose of Vehicles from 2D Vehicle PatchesThe International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 683-688.
    DOI: 10.5194/isprs-archives-XLIII-B2-2020-683-2020
  • Peters, T., Brenner, C., & Song, M. (2020): Improving Deep Learning based Semantic Segmentation with Multi View Outlier CorrectionThe International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 711-716.
    DOI: 10.5194/isprs-archives-XLIII-B2-2020-711-2020
  • Peters, T. & C. Brenner (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.) | Datei |
    ISBN: ISSN 0942-2870