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About

Kaisheng Xing is a robotics and computer vision researcher whose work centers on the intersection of industrial automation, 3D pose estimation, and intelligent grasping systems. His research addresses one of manufacturing's most persistent challenges: enabling industrial robots to accurately perceive, locate, and grasp irregular metal castings and roughcasts in complex, unstructured environments. Xing has made notable contributions by developing sophisticated six-degree-of-freedom (6DOF) pose measurement strategies that combine structured light scanning, stereo vision, point cloud processing, and deep learning techniques. His 2020 paper on boundary point cloud features for robotic grasping has garnered 9 citations, with subsequent work on deformable template matching and stereo vision-based measurement accumulating an additional 15 citations across three publications — demonstrating a steadily growing influence in the field. What distinguishes Xing's contributions is his persistent focus on real-world industrial applicability, particularly tackling the notoriously difficult problem of measuring reflective and geometrically irregular metal surfaces. His body of work represents a meaningful bridge between academic computer vision research and practical manufacturing robotics, offering solutions that advance the automation of traditionally manual and error-prone industrial processes.