Publications

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

Trusheim P., Mehltretter M., Rottensteiner F., Heipke C. (2022): Joint Estimation of Depth and its Uncertainty from Stereo Images using Bayesian Deep Learning,  ISPRS 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 Objects,  ISPRS 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 Analysis,  ISPRS 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 Matching,  ISPRS 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 Propagation,  ISPRS 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 Learning,  In: 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 Matching,  IEEE 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 Matching,  IEEE 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 Matching,  Bronx 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

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

Mehltretter, M., Heipke, C. (2018): Illumination Invariant Dense Image Matching based on Sparse Features,  38. Wissenschaftlich-Technische Jahrestagung der DGPF und PFGK18 Tagung in München, Band 27, 584-596

Rasho A., Dorozynski M.,Stracke J. and Mehltretter M. (2022): DEEP LEARNING-BASED TRACKING OF MULTIPLE OBJECTS IN THE CONTEXT OF FARM ANIMAL ETHOLOGY,  The 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 Estimation,  ISPRS Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 91-99
DOI: 10.5194/isprs-archives-XLIII-B2-2021-91-2021