Kaiyan Lin

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About

Kaiyan Lin’s research lies at the intersection of computer vision, robotics, and autonomous systems, with a focus on enabling machines to perceive and navigate the physical world. Their most influential work, a 2020 survey on monocular 3D object detection algorithms based on deep learning (20 citations), provides a comprehensive taxonomy of methods that allow autonomous vehicles and robots to infer spatial information from a single camera—a cost-effective alternative to LiDAR. This survey has become a foundational reference for researchers tackling 3D perception challenges. Lin also explores the application of computer vision in agriculture, as seen in their review of fruit-picking robots (4 citations), which bridges machine vision and robotic manipulation. Earlier work on complete coverage path planning and obstacle avoidance (2 citations) addresses fundamental problems in mobile robotics, proposing traversal algorithms that ensure efficient, collision-free navigation. Together, these contributions demonstrate Lin’s commitment to advancing perception and planning for real-world robotic systems, from autonomous driving to agricultural automation.