Jiexi Zhong
Papers
2
Total Citations
5
H-Index
2
About
Jiexi Zhong is a rising researcher in autonomous driving and mobile robotics, specializing in LiDAR-based perception. Their work focuses on moving object segmentation and semantic segmentation—two critical tasks for enabling safe, real-time navigation in dynamic environments. Zhong’s first major contribution, “StreamMOS,” introduces a streaming moving object segmentation framework that leverages multi-view perception and dual-span memory to enhance temporal reasoning from LiDAR sequences. This approach addresses a key limitation in prior methods, which often struggle to effectively transfer temporal cues across frames. Their second notable work, “4D-CS,” advances 4D spatio-temporal LiDAR semantic segmentation by exploiting cluster priors to improve the identification of both semantic classes and motion states for each point. Although these papers are very recent (2024), they have already garnered early citations (3 and 2, respectively), signaling growing recognition in the field. Zhong’s work is particularly impactful for students and researchers interested in the intersection of 3D vision, temporal modeling, and real-world autonomy. Their focus on exploiting spatio-temporal information and cluster-based priors represents a promising direction for making LiDAR perception more robust and efficient in complex, dynamic scenes.
Research Focus
Key Achievements
Top Papers
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- 2