Publikationen

begutachtete Veröffentlichungen

  • Trusheim P., Mehltretter M., Rottensteiner F., Heipke C. (2022): Joint Estimation of Depth and its Uncertainty from Stereo Images using Bayesian Deep LearningISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.69-78
    DOI: 10.5194/isprs-annals-V-2-2022-69-2022
  • Trusheim P., Mehltretter M., Rottensteiner F., Heipke C. (2022): Cooperative Visual Localisation Considering Dynamic ObjectsISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 169-177
    DOI: 10.5194/isprs-annals-V-1-2022-169-2022
  • Mehltretter M., Heipke C. (2021): Aleatoric Uncertainty Estimation for Dense Stereo Matching via CNN-based Cost Volume AnalysisISPRS Journal of Photogrammetry and Remote Sensing, pp. 63-75
    DOI: 10.1016/j.isprsjprs.2020.11.003
  • Zhong, Z., Mehltretter, M. (2021): Mixed Probability Models for Aleatoric Uncertainty Estimation in the Context of Dense Stereo MatchingISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.17-26
    DOI: 10.5194/isprs-annals-V-2-2021-17-2021
  • Höllmann, M., Mehltretter, M., Heipke, C. (2020): Geometry-Based Regularisation for Dense Image Matching via Uncertainty-Driven Depth PropagationISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. V-2-2020, S. 151–159
    DOI: 10.5194/isprs-annals-V-2-2020-151-2020
  • Mehltretter, M. (2020): Uncertainty Estimation for End-To-End Learned Dense Stereo Matching via Probabilistic Deep LearningIn: ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. V-2-2020, S. 161–169
    DOI: 10.5194/isprs-annals-V-2-2020- 161-2020
  • Mehltretter M. und Heipke C. (2019): CNN-based Cost Volume Analysis as Confidence Measure for Dense MatchingIEEE International Conference on Computer Vision Workshops (ICCV Workshops)
    DOI: 10.1109/ICCVW.2019.00262
  • Behmann N., Mehltretter M., Kleinschmidt S. P., Wagner B., Heipke C. und Blume H. (2018): GPU-enhanced Multimodal Dense MatchingIEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC)
    DOI: 10.1109/ NORCHIP.2018.8573526
  • Mehltretter M., Kleinschmidt S. P., Wagner B. und Heipke C. (2018): Multimodal Dense Stereo MatchingBronx T., Bruhn A. (Eds.): Pattern recognition – 40th German Conference GCPR Stuttgart, LNCS 11269, Springer, 407-421
    DOI: 10.1007/978-3-030-12939-2_28

Abschlussarbeiten

  • Mehltretter, M. (2021): Uncertainty Estimation for Dense Stereo Matching using Bayesian Deep Learning

Dissertation | Habilitation

  • Mehltretter, M. (2021): Uncertainty Estimation for Dense Stereo Matching using Bayesian Deep Learning

nicht begutachtete Veröffentlichungen

  • Mehltretter, M., Heipke, C. (2018): Illumination Invariant Dense Image Matching based on Sparse Features38. Wissenschaftlich-Technische Jahrestagung der DGPF und PFGK18 Tagung in München, Band 27, 584-596

Vorträge | Poster

  • Rasho A., Dorozynski M.,Stracke J. and Mehltretter M. (2022): DEEP LEARNING-BASED TRACKING OF MULTIPLE OBJECTS IN THE CONTEXT OF FARM ANIMAL ETHOLOGYThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    DOI: 10.5194/isprs-archives-XLIII-B2-2022-509-2022
  • Heinrich K., Mehltretter M. (2021): Learning Multi-Modal Features for Dense Matching-Based Confidence EstimationISPRS Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 91-99
    DOI: 10.5194/isprs-archives-XLIII-B2-2021-91-2021