Frank Michel
Papers
1
Total Citations
38
H-Index
1
About
Frank Michel is a leading researcher in computer vision, with a primary focus on 3D object pose estimation and its applications in robotics and augmented reality. His most influential work, "Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression" (2015, 38 citations), introduced a novel approach for accurately estimating the poses of articulated objects, such as drawers or robotic arms, by regressing object coordinates directly. This contribution is critical for enabling robots to interact with dynamic, multi-part environments—for instance, allowing a domestic robot to locate and retrieve an item from an open drawer. Michel’s research bridges the gap between geometric reasoning and practical robotic manipulation, advancing the field’s ability to handle complex, real-world scenarios. His work has been widely cited by subsequent studies in 6D pose estimation and robotic grasping, underscoring its foundational impact. By tackling the challenge of kinematic chains, Michel has helped lay the groundwork for more autonomous and context-aware robotic systems, making him a notable figure in applied computer vision.
Research Focus
Key Achievements
Top Papers
- 1