Luca Ebner
Papers
1
Total Citations
8
H-Index
1
About
Luca Ebner is a robotics researcher specializing in underwater perception and autonomous navigation, with a focus on enabling real-time 3D understanding for mobile underwater vehicles. His key research areas include monocular depth estimation, sensor fusion, and visual odometry for challenging subsea environments. Ebner’s most notable contribution is his work on metrically scaled monocular depth estimation, where he developed a deep learning model that fuses sparse depth measurements from triangulated features to resolve the inherent scale ambiguity in single-camera systems. This approach, detailed in his 2024 paper that has already garnered 8 citations, allows underwater robots to generate accurate, dense depth maps in real time without relying on expensive or bulky sensors. By integrating sparse priors from visual features, Ebner’s method significantly improves the reliability of depth predictions in low-visibility, dynamic underwater settings—a critical advancement for tasks like inspection, mapping, and autonomous navigation. His work bridges the gap between computer vision and field robotics, offering practical solutions for real-world deployment. Ebner’s research is increasingly recognized for its potential to enhance the autonomy and safety of underwater vehicles operating in GPS-denied environments.
Research Focus
Key Achievements
Top Papers
- 1