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About

Xiang Bai is a leading researcher in computer vision and intelligent systems, with key contributions spanning traffic sign detection, mobile robotics, and 3D scene understanding. His most cited work, a 2016 paper on traffic sign detection using fully convolutional networks (239 citations), pioneered a proposal-guided approach that significantly improved accuracy in autonomous driving applications. Bai also advanced indoor robotics with a depth-camera-based system for detecting and tracking multiple targets in cluttered environments (25 citations), addressing critical challenges like occlusion and appearance variation. His earlier research on contour grouping via local symmetry (17 citations) introduced novel Markov Chain Monte Carlo methods for shape extraction, while his work on UAV information processing (16 citations) integrated computer vision and geomatics for surveying and navigation. More recently, Bai has explored 2D image-based 3D scene retrieval (8 citations), enabling intuitive search of 3D datasets from single images. With a career spanning foundational shape analysis to cutting-edge deep learning, Bai’s research consistently bridges theoretical innovation and practical deployment, making him a key figure in visual perception and autonomous systems.