Carsten Steger
Papers
7
Total Citations
363
H-Index
5
About
Carsten Steger is a leading figure in industrial computer vision, renowned for his work in 3D object recognition and robotic calibration. His research centers on developing robust, practical algorithms for real-world manufacturing environments, bridging the gap between theoretical computer vision and industrial automation. Steger’s most significant contribution is the introduction of the MVTec ITODD dataset (159 citations), a benchmark that has become essential for advancing 3D object detection and pose estimation under realistic industrial conditions. He also pioneered a highly efficient approach for 3D object recognition by combining scale-space theory with similarity-based aspect graphs (139 citations), enabling fast and accurate pose determination from single camera images without relying on texture. In robotics, Steger has made critical advances in hand-eye calibration, particularly for SCARA robots, using dual quaternions to solve the unique kinematic constraints of these common industrial arms. His work is characterized by a deep commitment to solving practical challenges, from calibrating large-scale multi-camera surveillance systems to guiding robots with photogrammetric precision. With over 360 total citations, Steger’s research continues to shape the tools and methods that power modern industrial automation.
Research Focus
Key Achievements
Top Papers
- 1Introducing MVTec ITODD — A Dataset for 3D Object Recognition in Industry159 citations · 2017
- 2
- 3Hand-eye calibration of SCARA robots using dual quaternions34 citations · 2016
- 4
- 5Vision-guided robot calibration using photogrammetric methods10 citations · 2024
- 6Hand-Eye Calibration of SCARA Robots5 citations · 2015
- 7