Yueyang Ding
Papers
2
Total Citations
8
H-Index
2
About
Yueyang Ding is a researcher advancing the frontiers of 3D perception, with a focus on LiDAR point cloud processing for autonomous driving, robotics, and remote sensing. His work addresses critical challenges in object recognition and multi-object tracking (MOT) in dynamic environments. In his 2023 study, Ding proposed a novel point cloud object recognition method using histograms of dual deviation angle features, achieving robust classification for 3D perception tasks. Building on this, his 2024 research introduced an innovative framework for point cloud MOT that combines intra-frame graph structures with inter-frame bipartite graph matching, enhanced by re-identification (ReID) techniques to handle occlusions—a persistent hurdle in real-world tracking. Though early in his career, with his most-cited papers garnering 5 and 3 citations respectively, Ding’s contributions are already shaping practical solutions for smart cities and robotic navigation. His work stands out for its elegant integration of geometric feature extraction with graph-based tracking, offering a foundation for more resilient and accurate 3D perception systems.
Research Focus
Key Achievements
Top Papers
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