Chen Fu

Papers

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About

Chen Fu is a researcher specializing in depth perception, sensor fusion, and 3D scene reconstruction for autonomous systems. His work addresses a critical challenge in robotics and autonomous driving: achieving high-quality depth understanding without relying on prohibitively expensive hardware. Fu's most recognized contribution, "Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Camera" (2020), has garnered 39 citations and presents an innovative approach to combining affordable planar LIDAR sensors with RGB camera data to replicate the perceptual capabilities of costly high-definition LIDAR systems. This work has significant practical implications for commercial autonomous vehicles and small indoor robots operating under real-world budget constraints. Building on this foundation, his 2021 paper on linear inverse problem formulations for depth completion further refines sensor fusion techniques by leveraging sparse LIDAR point clouds alongside RGB imagery, offering a mathematically grounded framework for the field. Through these contributions, Fu has established himself as a meaningful voice in cost-effective perception solutions, helping democratize access to reliable depth sensing technology for a broader range of robotic and autonomous driving applications.