Biographie

seit 05.2020	Wissenschaftlicher Mitarbeiter am IPF (Machine Vision Metrology)
02.2020	Dissertation: "Selbstkalibrierung mobiler Multisensorsysteme mittels geometrischer 3D-Merkmale"
03.2016 - 02.2020	Doktorand und Wissenschaftlicher Mitarbeiter am IPF sowie externer Mitarbeiter am Fraunhofer Institut für Optronik, Systemtechnik und Bildauswertung (IOSB) in Ettlingen
2015	Masterarbeit: "Untersuchungen einer Erweiterung des Kanade-Lucas-Tomasi-Feature-Trackers für Bilder mit bekannter radialer Verzeichnung" am Fraunhofer Institut für Optronik, Systemtechnik und Bildauswertung (IOSB)
2014 - 2016	Wissenschaftliche Hilfskraft am Fraunhofer IOSB
2013 - 2015	Master-Studium der Geodäsie und Geoinformatik am KIT
2013	Bachelorarbeit: "Untersuchung zur Berechnung der Eigenbewegung eines Kinect-Sensors mit dem ICP-Algorithmus" am Institut für Photogrammetrie und Fernerkundung (IPF) des KIT
2010 - 2014	Wissenschaftliche Hilfskraft am Geodätischen Institut Karlsruhe (GIK) des KIT
2009 - 2013	Bachelor-Studium der Geodäsie und Geoinformatik am Karlsruher Institut für Technologie (KIT)

Forschungsschwerpunkte

Computer Vision und Maschinelles Lernen für industrielle Anwendungen
Bildbasierte 3D-Rekonstruktion und Szenenverständnis
Geometrische Sensorfusion, Kalibrierung und Robot Vision
Verlässliche KI: Unsicherheitsquantifizierung, Robustheit und Zuverlässigkeitsbewertung
Photogrammetrie und industrielle Messtechnik

Publikationen

2026

MVM-IOD: An Industrial Object-Centric Benchmark Dataset for the Evaluation of 3D Reconstruction Methods
Langendörfer, R.; Hillemann, M.; Ulrich, M.
2026, Juni 25. doi:10.35097/3untzs5utvkcxwm7
EMUFormer: Efficient Multi-task Uncertainties for Reliable Joint Semantic Segmentation and Monocular Depth Estimation
Landgraf, S.; Hillemann, M.; Kapler, T.; Ulrich, M.
2026. International Journal of Computer Vision, 134 (4), Article no: 142. doi:10.1007/s11263-026-02751-0
Recent Advances in Image-Based 3D Reconstruction: a Photogrammetric Perspective on Conventional and Learning-Based Techniques
Wang, X.; Wang, T.; Hillemann, M.; Yu, Y.; Shen, Z.; Zhan, Z.; Qin, R.; Ulrich, M.
2026. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. doi:10.1007/s41064-026-00412-y
MVM-IOD: An Industrial Object-Centric Benchmark Dataset for the Evaluation of 3D Reconstruction Methods
Langendörfer, R.; Hillemann, M.; Ulrich, M.
2026. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XI-2-2026, 689–696. doi:10.5194/isprs-annals-XI-2-2026-689-2026
Uncertainty Quality of VGGT: An Analysis on the DTU Benchmark Dataset
Hillemann, M.; Langendörfer, R.; Landgraf, S.; Ulrich, M.
2026. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XI-2-2026, 665–672. doi:10.5194/isprs-annals-XI-2-2026-665-2026
Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustness
Landgraf, S.; Hillemann, M.; Ulrich, M.
2026. Pattern Recognition : 47th DAGM German Conference, DAGM GCPR 2025, Freiburg, Germany, September 23–26, 2025, Proceedings. Ed.: M. Keuper, 434–452, Springer Nature Switzerland. doi:10.1007/978-3-032-12840-9_28

2025

FeatureGS: Eigenvalue-feature optimization in 3D Gaussian Splatting for geometrically accurate and artifact-reduced reconstruction
Jäger, M.; Hillemann, M.; Jutzi, B.
2025. ISPRS Open Journal of Photogrammetry and Remote Sensing, 17, 100100. doi:10.1016/j.ophoto.2025.100100
A comparative study on multi-task uncertainty quantification in semantic segmentation and monocular depth estimation
Landgraf, S.; Hillemann, M.; Kapler, T.; Ulrich, M.
2025. tm - Technisches Messen, 92 (7-8), 298–310. doi:10.1515/teme-2025-0004
Industry-relevant benchmark dataset for the evaluation of 3D reconstruction methods and evaluation of different NeRF methods
Langendörfer, R.; Hillemann, M.; Ulrich, M.
2025. Dreiländertagung der OVG, DGPF und SGPF - Photogrammetrie - Fernerkundung - Geoinformation. 45. Jahrestagung der DGPF in Muttenz / Basel 2025. Hrsg.: T. P. Kersten, 99–112, Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF). doi:10.24407/KXP:1928479294
A Novel Correspondence Model for Linking Objects and Texts in Construction Plans
Hong, S.; Landgraf, S.; Hillemann, M.; Ulrich, M.
2025. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-G-2025, 597–604. doi:10.5194/isprs-archives-XLVIII-G-2025-597-2025
Evaluation of Semi-supervised Semantic Segmentation for Remote Sensing, Medical Imaging, and Machine Vision Settings
Landgraf, S.; Huber, J.; Hillemann, M.; Ulrich, M.
2025. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-G-2025, 527–534. doi:10.5194/isprs-annals-X-G-2025-527-2025
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation
Landgraf, S.; Hillemann, M.; Kapler, T.; Ulrich, M.
2025. Pattern Recognition – 46th DAGM German Conference, DAGM GCPR 2024, Munich, Germany, September 10–13, 2024, Proceedings, Part II. Ed.: D. Cremers, 348–364, Springer Nature Switzerland. doi:10.1007/978-3-031-85187-2_22
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation
Landgraf, S.; Hillemann, M.; Kapler, T.; Ulrich, M.
2025. arxiv. doi:10.5445/IR/1000178524

2024

HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2
Jäger, M.; Kapler, T.; Feßenbecker, M.; Birkelbach, F.; Hillemann, M.; Jutzi, B.
2024. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-2-2024, 159 – 166. doi:10.5194/isprs-archives-XLVIII-2-2024-159-2024
Uncertainty Quantification with Deep Ensembles for 6D Object Pose Estimation
Wursthorn, K.; Hillemann, M.; Ulrich, M.
2024. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-2-2024, 223–230. doi:10.5194/isprs-annals-X-2-2024-223-2024
Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications
Hillemann, M.; Langendörfer, R.; Heiken, M.; Mehltretter, M.; Schenk, A.; Weinmann, M.; Hinz, S.; Heipke, C.; Ulrich, M.
2024. arxiv. doi:10.48550/arXiv.2405.04345
DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation
Landgraf, S.; Wursthorn, K.; Hillemann, M.; Ulrich, M.
2024. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 92 (2), 101–114. doi:10.1007/s41064-024-00280-4
HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2
Jäger, M.; Kapler, T.; Feßenbecker, M.; Birkelbach, F.; Hillemann, M.; Jutzi, B.
2024. doi:10.48550/arXiv.2405.02005
Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications
Hillemann, M.; Langendörfer, R.; Heiken, M.; Mehltretter, M.; Schenk, A.; Weinmann, M.; Hinz, S.; Heipke, C.; Ulrich, M.
2024. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-2-2024, 137–144. doi:10.5194/isprs-archives-XLVIII-2-2024-137-2024
Uncertainty-aware Cross-Entropy for Semantic Segmentation
Landgraf, S.; Hillemann, M.; Wursthorn, K.; Ulrich, M.
2024. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-2-2024, 129–136. doi:10.5194/isprs-annals-X-2-2024-129-2024
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation
Landgraf, S.; Hillemann, M.; Kapler, T.; Ulrich, M.
2024. arxiv. doi:10.48550/arXiv.2402.10580
Uncertainty-Aware Hand–Eye Calibration
Ulrich, M.; Hillemann, M.
2024. IEEE Transactions on Robotics, 40, 573–591. doi:10.1109/TRO.2023.3330609

2023

Semantic Mapping and Autonomous Navigation for Agile Production System
Zhou, B.; Klein, J.-F.; Wang, B.; Hillemann, M.
2023. 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), Auckland, New Zealand, 26-30 August 2023, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CASE56687.2023.10260623
Dataset for the Segmentation of Industrial Burner Flames
Landgraf, S.; Hillemann, M.; Ulrich, M.; Aberle, M.; Jung, V.
2023, Juni 20. doi:10.5445/IR/1000159497
U-CE: Uncertainty-aware Cross-Entropy for Semantic Segmentation
Landgraf, S.; Hillemann, M.; Wursthorn, K.; Ulrich, M.
2023. arxiv. doi:10.48550/arXiv.2307.09947
Segmentation of Industrial Burner Flames: A Comparative Study from Traditional Image Processing to Machine and Deep Learning
Landgraf, S.; Hillemann, M.; Aberle, M.; Jung, V.; Ulrich, M.
2023. doi:10.5445/IR/1000159876
DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation
Landgraf, S.; Wursthorn, K.; Hillemann, M.; Ulrich, M.
2023

2022

Beat the MArVIN - Industrieroboter MArVIN lernt Geschicklichkeitsspiel "der heiße Draht"
Wursthorn, K.; Hillemann, M.; Ulrich, M.
2022. doi:10.5445/IR/1000150338
Evaluation of self-supervised learning approaches for semantic segmentation of industrial burner flames
Landgraf, S.; Kühnlein, L.; Hillemann, M.; Hoyer, M.; Keller, S.; Ulrich, M.
2022. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2022, 601–607. doi:10.5194/isprs-archives-XLIII-B2-2022-601-2022
Comparison of uncertainty quantification methods for CNN-based regression
Wursthorn, K.; Hillemann, M.; Ulrich, M.
2022. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2022, 721–728. doi:10.5194/isprs-archives-XLIII-B2-2022-721-2022

2021

Generic Hand–Eye Calibration of Uncertain Robots
Ulrich, M.; Hillemann, M.
2021. IEEE International Conference on Robotics and Automation (ICRA), 30 May-5 June 2021, 11060–11066, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA48506.2021.9560823

2020

Selbstkalibrierung mobiler Multisensorsysteme mittels geometrischer 3D-Merkmale. Dissertation
Hillemann, M.
2020, November 2. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000125412

2019

Impact of different trajectories on extrinsic self-calibration for vehicle-based mobile laser scanning systems
Hillemann, M.; Meidow, J.; Jutzi, B.
2019. The international archives of photogrammetry, remote sensing and spatial information sciences, XLII-2/W16, 119–125. doi:10.5194/isprs-archives-XLII-2-W16-119-2019
Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features
Hillemann, M.; Weinmann, M.; Mueller, M. S.; Jutzi, B.
2019. Remote sensing, 11 (16), Article: 1955. doi:10.3390/rs11161955

2018

Combining independent visualization and tracking systems for augmented reality
Hübner, P.; Weinmann, M.; Hillemann, M.; Jutzi, B.; Wursthorn, S.
2018. 2018 ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 455–462, International Society for Photogrammetry and Remote Sensing (ISPRS). doi:10.5194/isprs-archives-XLII-2-455-2018

2017

UCalMiCeL - Unified Intrinsic and Extrinsic Calibration of a Multi-Camera-System and a Laserscanner
Hillemann, M.; Jutzi, B.
2017. 4th ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences - International Conference on Unmanned Aerial Vehicles in Geomatics, Bonn, Germany, 4.-7. September 2017. Ed.: C. Stachniss. Vol. IV-2/W3., 49–57, ISPRS. doi:10.5194/isprs-annals-IV-2-W3-17-2017
Point cloud analysis for UAV-borne laser scanning with horizontally and vertically oriented line scanners - Concept and first results
Weinmann, M.; Müller, M. S.; Hillemann, M.; Reydel, N.; Hinz, S.; Jutzi, B.
2017. 4th ISPRS International Conference on Unmanned Aerial Vehicles in Geomatics, Bonn, Germany, 4th - 7th September 2017, 399–406, International Society for Photogrammetry and Remote Sensing (ISPRS). doi:10.5194/isprs-archives-XLII-2-W6-399-2017

2016

Concept for an airborne real-time ISR system with multi-sensor 3D data acquisition
Haraké, L.; Schilling, H.; Blohm, C.; Hillemann, M.; Lenz, A.; Becker, M.; Keskin, G.; Middelmann, W.
2016. Electro-Optical and Infrared Systems: Technology and Applications XIII, SPIE Security + Defence, 2016, Edinburgh, United Kingdom, 26-29 September 2016. Ed.: D. A. Huckridge, 998709, Society of Photo-optical Instrumentation Engineers (SPIE). doi:10.1117/12.2241051