Akihiro Sugimoto
Papers
6
Total Citations
129
H-Index
5
About
Akihiro Sugimoto is a leading researcher in computer vision and human-robot interaction, whose work bridges the gap between geometric perception and intelligent systems. His major contributions span hand-eye calibration, 3D scene understanding, and human gaze analysis. Notably, his 2011 paper on structure-from-motion based hand-eye calibration using L-infinity minimization (46 citations) provides a robust method for calibrating camera-robot systems without physical targets, a critical advance for real-world robotics. His 2017 work on fast 3D point cloud segmentation using supervoxels (45 citations) enables efficient scene parsing by integrating geometry and color, directly benefiting autonomous navigation and manipulation. Sugimoto also explores human-centered AI, as seen in his 2016 study on gaze sensing and control for human-harmonized environments, and his 2019 research on video semantic salient instance segmentation (11 citations), which focuses on detecting only task-relevant, salient objects for applications like self-driving cars. His recent work on temporal feature enhancement for live-stream video detection (2022) continues to push boundaries in real-time perception. With over 130 citations across his top papers, Sugimoto’s research consistently advances practical, scalable vision systems that harmonize with human behavior.
Research Focus
Key Achievements
Top Papers
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- 3Look who's talking12 citations · 2016
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