Saggi Eppel
Papers
1
Total Citations
6
H-Index
1
About
Saggi Eppel is a leading researcher in computer vision and robotics, specializing in the perception of transparent objects—a notoriously challenging problem for autonomous systems. His major contribution, the MVTrans framework (2023), introduced a multi-view approach that leverages RGB-D and stereo inputs to simultaneously estimate depth, pose, and shape of transparent objects, overcoming the limitations of single-view methods that fail due to light refraction and lack of texture. This work has garnered 6 citations and is foundational for enabling reliable robot manipulation in environments like households and laboratories, where glassware and clear containers are common. Eppel’s research addresses a critical gap in perception, moving beyond simple detection to full 3D understanding, which is essential for tasks ranging from automated dishwashing to chemical handling. His innovative use of multi-view geometry and deep learning has set a new standard in the field, making him a key figure in advancing transparent object perception for practical robotics applications.
Research Focus
Key Achievements
Top Papers
- 1MVTrans: Multi-View Perception of Transparent Objects6 citations · 2023