Alexander Haberl
Papers
1
Total Citations
4
H-Index
1
About
Alexander Haberl is a researcher at the forefront of computer vision and robotics, specializing in the notoriously difficult problem of perceiving transparent objects. His key research areas include 6D pose estimation, refraction-guided learning, and intermediate representation learning for complex visual scenes. Haberl’s major contribution, the ReFlow6D framework, introduces a novel approach that leverages refraction patterns to estimate the full 3D position and 3D orientation of transparent objects—a task where traditional methods fail due to the lack of surface texture. By learning intermediate representations from refracted light, his work bridges the gap between raw sensor data and robust pose estimation, directly enabling more reliable robotic manipulation of everyday items like glassware. Already garnering 4 citations since its 2024 publication, ReFlow6D is recognized as a significant step forward in handling specular and transparent surfaces. Haberl’s research is particularly notable for its practical impact, addressing a critical bottleneck in deploying robots in kitchens, laboratories, and manufacturing lines where transparent objects are ubiquitous. His work promises to unlock new capabilities in autonomous systems, making him a rising voice in the intersection of geometric computer vision and embodied AI.
Research Focus
Key Achievements
Top Papers
- 1