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About

Wuyuan Xie is a researcher at the forefront of 3D vision and efficient deep learning, with a focus on enabling intelligent systems for autonomous driving and robotics. His key research areas include LiDAR point cloud processing, point cloud quality assessment, and hardware-efficient neural network design. Xie’s major contributions include developing a task-driven, scene-aware LiDAR point cloud coding framework that addresses the critical bandwidth bottleneck in autonomous vehicle communication, a work that has garnered 31 citations. He has also pioneered a large language model (LLM)-guided cross-modal approach for point cloud quality assessment, leveraging graph learning to ensure reliability in applications like virtual reality and 3D reconstruction (15 citations). Additionally, Xie introduced BinaryFormer, a hierarchical-adaptive binary Vision Transformer that dramatically reduces computational costs while maintaining high performance in object recognition and robot control. His work bridges the gap between real-world sensing demands and efficient AI deployment, making him a notable figure in advancing practical, scalable solutions for autonomous systems.