Hauke Kaulbersch
Papers
1
Total Citations
20
H-Index
1
About
Hauke Kaulbersch is a researcher specializing in computer vision and autonomous systems, with a particular focus on real-time 3D object detection and semantic understanding. His most notable contribution is the development of Complexer-YOLO, a pioneering framework that fuses neural network-based 3D detection with visual semantic segmentation on point cloud data. This work, published in 2019, addresses a fundamental challenge in autonomous driving and robotics by enabling accurate, real-time 3D object detection and tracking on semantic point clouds—a critical capability for safe navigation in dynamic environments. With 20 citations, Complexer-YOLO has influenced subsequent research in efficient 3D perception for self-driving cars, augmented reality, and robotics applications. Kaulbersch’s work exemplifies the integration of deep learning with geometric reasoning, pushing the boundaries of what is achievable in real-time scene understanding. His research continues to impact the development of robust perception systems that must operate under strict latency constraints, making him a key contributor to the advancement of autonomous vehicle technology and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1