Papers
11
Total Citations
107
H-Index
6
About
Yoichi Sato is a researcher whose work spans 3D computer vision, human-computer interaction, and egocentric perception, with particular expertise in shape reconstruction, hand pose estimation, and gaze sensing. His early contributions in the 2000s laid important groundwork in active rangefinding and 3D object recognition, notably developing a compact rangefinder system using a CCD camera, galvano mirror, and semiconductor laser for precise 3D shape reconstruction (32 citations), alongside robust localization methods employing local EGI and M-estimators for robotic applications in real-world electric distribution systems. More recently, Sato has made significant strides in understanding human hand-object interactions from an egocentric perspective, contributing influential survey work on efficient annotation and learning for 3D hand pose estimation (19 citations) and pioneering fine-grained affordance annotation frameworks that support action anticipation and robot imitation learning (12 citations). His involvement in benchmark challenges for egocentric hand interactions and surgical tool localization further underscores his commitment to advancing evaluation standards in the field. His research on gaze sensing in daily living environments reflects a broader vision of harmonized human-information systems. Collectively, Sato's work bridges fundamental 3D vision techniques with emerging applications in AR/VR, robotics, and assistive technologies.
Research Focus
Key Achievements
Top Papers
- 1Three-dimensional shape reconstruction by active rangefinder32 citations · 2002
- 2Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey19 citations · 2023
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- 7Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-20256 citations · 2023
- 8Object recognition using local EGI and 3D models with M-estimators5 citations · 2003
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