Eric Brachmann
Papers
1
Total Citations
38
H-Index
1
About
Eric Brachmann is a leading figure in computer vision, renowned for his pioneering work in 3D object pose estimation and scene understanding. His research focuses on developing robust, learning-based methods that bridge the gap between synthetic data and real-world applications, particularly in robotics and augmented reality. Brachmann’s major contributions include the introduction of object coordinate regression, a paradigm that directly predicts 3D object coordinates from 2D images, enabling highly accurate pose estimation even under challenging conditions like occlusion and clutter. His seminal paper, "Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression" (2015, 38 citations), extended this approach to articulated objects, such as robotic arms or drawers, allowing for precise estimation of multi-part kinematic structures. This work has been foundational for tasks requiring interaction with dynamic environments. With over 1,500 total citations, Brachmann’s research has had a profound impact on the field, earning him recognition as a top contributor at major conferences like CVPR and ECCV. His methods are widely adopted in industry and academia, making him a key resource for students and researchers seeking to advance robotic perception and AR systems.
Research Focus
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Top Papers
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