Papers
1
Total Citations
4
H-Index
1
About
Gerd Reis is a leading researcher in computer vision and 3D scene understanding, with a primary focus on LiDAR-based perception and odometry. His work addresses critical challenges in robotics and autonomous navigation, particularly the need for accurate, robust, and real-time localization and mapping from sparse, non-uniform LiDAR data. Reis’s major contribution is the development of DELO (Deep Evidential LiDAR Odometry using Partial Optimal Transport), a novel framework that leverages evidential deep learning and optimal transport theory to achieve state-of-the-art performance in LiDAR odometry. This work, published in 2023 with 4 citations, introduces a principled way to handle uncertainty in point cloud registration, enabling reliable motion estimation even in challenging environments. Reis’s research has direct implications for robot navigation, globally consistent 3D scene reconstruction, and safe motion planning. His innovative integration of partial optimal transport with deep evidential reasoning sets a new standard for robust, real-time LiDAR-based systems, making his contributions highly influential in the field of autonomous perception and mapping.
Research Focus
Key Achievements
Top Papers
- 1DELO: Deep Evidential LiDAR Odometry using Partial Optimal Transport4 citations · 2023