Kohsuke Mano

Papers

Total Citations

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About

Kohsuke Mano is a researcher in computer vision and robotics, with a primary focus on 6D object pose estimation—a critical capability for robotic grasping and manipulation. His most cited work, "Iterative Coarse-to-Fine 6D-Pose Estimation Using Back-propagation" (2021), introduces a novel method that estimates an object’s full 3D position and orientation from a single RGB image. Unlike many deep learning approaches that rely on fixed training viewpoints and struggle with domain shifts, Mano’s technique iteratively refines pose estimates through a coarse-to-fine back-propagation framework, enhancing robustness to changes in object appearance, lighting, or background. This contribution addresses a key limitation in existing methods, making pose estimation more reliable for real-world robotic tasks. With 5 citations, his work is gaining traction among researchers seeking practical, generalizable solutions in 6D pose estimation. Mano’s research sits at the intersection of computer vision and robotics, aiming to bridge the gap between simulation-trained models and real-world deployment. His approach offers a promising direction for improving autonomous systems that require precise object interaction.