Prof. Dr.-Ing.  Markus Ulrich

Prof. Dr.-Ing. Markus Ulrich

  • Englerstr. 7
    76131 Karlsruhe


Since 1.4.2020 Professor for Machine Vision Metrology at the Institute for Photogrammetry and Remote Sensing of the Karlsruhe Institute of Technology (KIT)
2017 – 2019 Privatdozent at the KIT-Department of Civil Engineering, Geo and Environmental Sciences
2013 – 2020 Invention and patent manager at MVTec Software GmbH, Munich
2013 – 2017 Lecturer for the subject "Industrial Image Processing and Machine Vision " KIT-Department of Civil Engineering, Geo and Environmental Sciences
2008 – 2020 Head of the research team at MVTec
2005 – 2020 Lecturer for the subject "Close-Range Photogrammetry" at the Department of Civil, Geo and Environmental Engineering at the Technical University of Munich (TUM)
2003 – 2008 Software Engineer at MVTec
2000 – 2003 Research associate and PhD student at the Chair of Photogrammetry and Remote Sensing of the TUM in cooperation with MVTec


Scientific Qualification

1.2.2017 Postdoctoral lecture qualification (Habilitation) and award of teaching authorization for the subject "Machine Vision" at the KIT-Department of Civil Engineering, Geo and Environmental Sciences
26.6.2003 PhD degree (Dr.-Ing.) at the TUM Department of Civil, Geo and Environmental Engineering
23.3.2000 Diploma degree (Dipl.-Ing) in Geodesy
1995 - 2000 Study of Geodesy at TUM


Main Research Topics

Machine Vision is a multifaceted discipline and includes aspects from optics (e.g. illumination, lenses), electrical engineering (e.g. sensor technology), mechanical engineering (e.g. industrial robots, optical inspection machines), computer science and software engineering (e.g. efficient implementations of innovative computer vision algorithms). This is also reflected in the research topics:

  • Reliable detection and accurate position measurement of objects in images and 3D sensor data
  • Camera models and calibration
  • Machine learning in industrial applications for object inspection and robotics
  • Object identification in images
  • Surface inspection of objects in 2D and 3D sensor data
  • Hand-eye calibration of industrial robots

Our research work often focuses on geodetic aspects such as reliability or accuracy. At the same time, 20 years of industrial experience serves as an important guide and ensures the innovative power of the newly developed processes.


Teaching about methods and technologies that are actually used in practice in the professional field is important to provide a sound insight into machine vision metrology. Only in close exchange with the professional field can the requirements of the technologies be sufficiently considered and the students be taught appropriate skills. These requirements are therefore already imparted in the teaching and should play a fundamental and at the same time motivating role. The industrial context is also emphasized by exercises that are relevant in real applications.

The references of machine vision metrology to "classical" geodesy are emphasized and geodetic aspects (e.g. accuracy considerations and reliability statements) are explicitly considered. Within the framework of research-oriented teaching, students are involved in current research projects at the institute, which also allows scientific methods to be taught.


Scientific Publications


Further Publications

Dataset for the Segmentation of Industrial Burner Flames
Landgraf, S.; Hillemann, M.; Ulrich, M.; Aberle, M.; Jung, V.
2023, June 20. doi:10.5445/IR/1000159497
Geodäsie und Geoinformatik am KIT studieren
Dalheimer, L.; Fuge, R.; Gschwind, C.; Juretzko, M.; Landgraf, S.; Meid, F.; Naab, C.; Ulrich, M.; Weisgerber, J.
2021. doi:10.5445/IR/1000137359


  • Hand-eye calibration of camera-guided devices
    • EP 4094897 (publication date: 20.9.2023)
  • System and method for model adaptation
    • JP 6612822 (publication date: 27.11.2019)
    • US 10460472 (publication date: 29.10.2019)
  • System and method for efficient 3D reconstruction of objects with telecentric line-scan cameras
    • EP 3896640 (publication date: 28.9.2022)
    • US 11328478 (publication date: 10.5.2022)
  • Recognition and pose determination of 3D objects in multimodal scenes
    • JP 6216508 (publication date: 18.10.2017)
    • CN 103729643 (publication date: 12.09.2017)
    • US 8994723 (publication date: 31.5.2015)
    • EP 2720171 (publication date: 8.4.2015)
  • Recognition and pose determination of 3D objects in 3D scenes
    • CN 102236794 (publication date: 4.3.2015)
    • JP 5677798 (publication date: 25.2.2015)
    • US 8830229 (publication date: 9.9.2014)
    • EP 2385483 (publication date: 21.11.2012)
  • System and method for 3D object recognition
    • CN 101408931 (publication date: 20.2.2013)
    • US 8379014 (publication date: 19.2.2013)
    • EP 2048599 (publication date: 16.12.2009)
    • JP 4785880 (publication date: 30.4.2009)
  • System and methods for automatic parameter determination in machine vision
    • US 7953290 (publication date: 31.5.2011)
    • US 7953291 (publication date: 31.5.2011)
    • US 7751625 (publication date: 6.6.2010)
    • JP 4907219 (publication date: 4.10.2007)
  • Hierarchical component-based object recognition
    • JP 5330579 (publication date: 1.11.2012)
    • EP 1394727 (publication date: 12.10.2011)
    • JP 4334301 (publication date: 30.9.2009)
    • JP 5329254 (publication date: 14.5.2009)
    • US 7239929 (publication date: 3.7.2007)