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About

Rui She is an emerging researcher specializing in 3D computer vision, LiDAR-based localization, and autonomous systems, with a focus on developing robust algorithms for real-world perception challenges. Her work sits at the intersection of deep learning and spatial understanding, addressing critical problems in robotics, autonomous driving, and 3D scene analysis. She has made notable contributions through innovative architectural designs that push the boundaries of conventional approaches. Her most influential work, *PointDifformer* (2024, 27 citations), introduces a pioneering combination of neural diffusion processes and transformer architectures to achieve robust point cloud registration under noisy and perturbed conditions — a persistent challenge in the field. Her *HypLiLoc* framework (2023, 19 citations) demonstrates creative thinking by leveraging hyperbolic geometry for LiDAR pose regression, significantly improving accuracy and efficiency in relocalization tasks where traditional methods falter. More recently, *PRFusion* (2024) advances multi-modal place recognition by effectively fusing image and point cloud data to enhance robustness across diverse environments. With a growing citation record and research spanning cutting-edge topics in 3D perception and sensor fusion, Rui She is establishing herself as a promising voice in the autonomous systems and computer vision communities.