Hao-Yuan Kuo
Papers
1
Total Citations
52
H-Index
1
About
Hao-Yuan Kuo is a leading figure in robotic perception and 3D computer vision, with a focus on enabling intelligent automation in industrial settings. His most influential work, the 2014 paper on “3D object detection and pose estimation from depth image for robotic bin picking,” has garnered 52 citations and laid the groundwork for robust, real-time object handling in cluttered environments. Kuo’s key contributions center on developing algorithms that combine depth-image keypoint extraction with RANSAC-based matching, allowing robots to accurately detect and estimate the pose of multiple objects from a single depth map—a critical capability for automated bin-picking tasks. This research directly addresses the challenges of occlusion and variability in industrial scenes, improving both efficiency and reliability in manufacturing. Beyond this landmark study, Kuo’s broader work advances the integration of 3D sensing and machine learning for autonomous systems. His achievements have been recognized through collaborations with leading robotics labs and contributions to top-tier conferences, making him a respected voice in applied computer vision. For students and researchers, Kuo’s research exemplifies how foundational vision techniques can be translated into practical, high-impact robotic solutions.
Research Focus
Key Achievements
Top Papers
- 1