Shao‐Yi Chien
Papers
7
Total Citations
80
H-Index
6
About
Shao-Yi Chien’s research lies at the intersection of computer vision, robotics, and human-robot interaction, with a particular focus on object pose estimation and multi-robot control systems. His most cited work, “A Benchmark Dataset for 6DoF Object Pose Tracking” (29 citations), provides a critical evaluation framework for tracking the six-degree-of-freedom pose of objects in real scenes—a foundational task for augmented reality and robotics applications. Chien has advanced direct pose estimation techniques for planar objects, developing methods that bypass traditional feature extraction to achieve robust 3D pose estimation from single 2D images, as demonstrated in his 2016 and 2018 papers. In the domain of robot team coordination, his 2011 study on alarm-driven supervisory control (17 citations) explores how annunciator systems can direct human attention to manage large robot teams effectively, while his work on asynchronous control with image queues addresses the challenge of presenting operators with relevant visual data in urban search and rescue scenarios. Chien has also contributed to cooperative surveillance systems, integrating fixed camera localization with mobile robot tracking. His research has garnered over 80 citations, reflecting its practical impact on autonomous systems and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1[POSTER] A Benchmark Dataset for 6DoF Object Pose Tracking29 citations · 2017
- 2Effects of Alarms on Control of Robot Teams17 citations · 2011
- 3Direct pose estimation for planar objects10 citations · 2018
- 4
- 5Asynchronous Control with ATR for Large Robot Teams7 citations · 2011
- 6Direct 3D pose estimation of a planar target6 citations · 2016
- 7