Papers
5
Total Citations
85
H-Index
3
About
Chin-Chia Wu is a leading researcher in robotics and computer vision, with a focus on 3D perception, object manipulation, and real-time visual inspection. His most influential work, “3D object detection and pose estimation from depth image for robotic bin picking” (52 citations), introduced a robust system that uses keypoint matching and RANSAC on single depth maps to enable automated bin-picking—a critical capability for industrial robotics. Wu has also made significant contributions to accelerating computer vision algorithms in hardware; his 2012 paper on a real-time FPGA-based template matching module (17 citations) demonstrated how to dramatically speed up normalized cross-correlation for visual inspection tasks. In the realm of 3D reconstruction, Wu conducted rigorous error analysis of the KinectFusion algorithm using a sensor model (10 citations), providing valuable insights into the accuracy of RGB-D sensors. His earlier work on multi-camera calibration and robot localization (2010, 3 citations) fused visual and inertial data for autonomous navigation, while his 2009 paper (3 citations) introduced a particle-filter-based approach for simultaneous camera calibration and mobile robot localization. Across these contributions, Wu has advanced the practical deployment of vision systems in robotics, from factory floors to autonomous navigation.
Research Focus
Key Achievements
Top Papers
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- 3Error analysis and experiments of 3D reconstruction using a RGB-D sensor10 citations · 2014
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