Johannes Exner
Papers
1
Total Citations
6
H-Index
1
About
Johannes Exner is a researcher at the intersection of robotics, computer vision, and 3D geometry processing, with a focus on shape modeling and perception. His most cited work, "Visual and tactile 3D point cloud data from real robots for shape modeling and completion" (2020, 6 citations), addresses a critical challenge in robotics: how to extract reliable 3D object shape information from noisy, real-world sensory data. Exner’s contribution lies in bridging visual and tactile sensing—integrating point cloud data from cameras and robot touch to improve shape reconstruction and completion, even under uncertainty. This work is foundational for applications like robotic manipulation, where accurate object geometry is essential. By demonstrating that real robot data can be used effectively for 3D modeling, Exner has advanced the practical deployment of perception systems in unstructured environments. His research speaks to students and engineers seeking to merge computer graphics, vision, and robotics for robust, real-world performance.
Research Focus
Key Achievements
Top Papers
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