Junyi Wang

Beijing University of Chemical Technology, Peng Cheng Laboratory

Papers

Total Citations

H-Index

About

Junyi Wang is a robotics and computer vision researcher whose work focuses on enabling machines to perceive and interact with the physical world more intelligently. His primary research areas include transparent object perception, visual localization, and human-aware tracking for autonomous systems. Wang’s most influential contribution is a novel method for detecting and locating transparent objects using RGB-D cameras—a notoriously difficult problem in robotics. By fusing depth, RGB, and infrared imagery, his 2019 paper (30 citations) significantly improved robot grasping accuracy for glass and plastic items, directly impacting industrial automation and service robotics. He has also advanced visual camera relocalization by combining hand-crafted features with learned representations (2023, 14 citations), bridging classical and deep learning approaches for robust navigation. More recently, Wang has tackled human target tracking with an improved YOLOv7 algorithm (2023), enhancing detection accuracy for security and medical applications. His work consistently addresses real-world perception challenges, from transparent surfaces to dynamic human motion, earning recognition for its practical impact. Wang’s research continues to push the boundaries of what robots can see and grasp.