Johannes Feulner
Papers
1
Total Citations
6
H-Index
1
About
Johannes Feulner is a computer vision researcher whose work focuses on advancing 3D reconstruction and scene understanding using depth-sensing technologies. His most notable contribution is a pioneering algorithm for robust, real-time 3D modeling of static scenes using solely a Time-of-Flight (ToF) sensor, without relying on additional hardware like inertial sensors or positioning robots. This work, published in 2009, demonstrated that accurate 3D models could be generated from arbitrary camera movement using only range and intensity data, laying groundwork for more accessible and cost-effective 3D scanning. While his citation count of 6 for this paper may seem modest, it reflects early-stage work in a niche area that has since grown significantly. Feulner’s research addresses fundamental challenges in real-time 3D perception, including sensor noise, motion estimation, and data fusion, with implications for robotics, augmented reality, and autonomous navigation. His approach to leveraging minimal sensor input for maximal geometric fidelity remains relevant as ToF sensors become ubiquitous in consumer devices.
Research Focus
Key Achievements
Top Papers
- 1