Linqing Zhao

Papers

Total Citations

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About

Linqing Zhao is a rising star in computer vision and robotics, whose research focuses on enabling machines to perceive and interact with dynamic 3D environments in real time. His most cited work, "Memory-based Adapters for Online 3D Scene Perception" (2024, 6 citations), tackles a critical limitation of conventional 3D perception methods: their reliance on pre-reconstructed static scenes. Zhao introduces a novel framework that processes streaming RGB-D video input, using memory-based adapters to incrementally build and update a 3D scene understanding without requiring offline reconstruction. This breakthrough is particularly impactful for robotic applications, where agents must navigate and manipulate objects in real time based on live sensor data. By bridging the gap between offline 3D perception and online, interactive systems, Zhao’s work lays the groundwork for more adaptive and responsive autonomous agents. Though early in his career, his contributions signal a shift toward memory-augmented, temporally aware perception—a key enabler for next-generation embodied AI.