Markus Rauhut

Fraunhofer Institute for Industrial Mathematics

Papers

Total Citations

H-Index

About

Markus Rauhut is a researcher whose work sits at the intersection of computer vision, robotics, and industrial quality control. His primary research focus is on automating visual surface inspection—a critical process for detecting manufacturing defects. Rauhut’s most cited paper, “Feature-Driven Viewpoint Placement for Model-Based Surface Inspection” (2020, 29 citations), tackles a core challenge: how to optimally position a camera to capture the most informative views of a complex 3D object. Rather than relying on brute-force scanning, his approach uses a model of the object to intelligently plan viewpoints, dramatically improving detection efficiency and coverage. This work has direct implications for automated inspection systems in industries like automotive and aerospace, where even microscopic surface flaws can have serious consequences. Rauhut’s contributions are helping to bridge the gap between theoretical computer vision and practical, deployable robotic inspection. With a growing citation record, his research is gaining traction among both academic and industrial engineers seeking to make quality control faster, more reliable, and less dependent on human visual inspection.