Zhewen Yan
Papers
2
Total Citations
10
H-Index
2
About
Zhewen Yan is a rising roboticist whose research focuses on real-time dense mapping and autonomous exploration for large-scale 3D environments, with key contributions at the intersection of LiDAR, visual perception, and neural implicit representations. In their highly cited work "Rapid-Mapping," Yan introduced a LiDAR-visual implicit neural representation framework that achieves real-time dense mapping with high-fidelity textures—a breakthrough for robots, digital twins, and AR/VR applications where memory efficiency and detail are critical. This paper has already garnered 6 citations since 2024, reflecting its immediate impact. Yan also addresses the scalability challenge in autonomous exploration with "Efficient UAV Exploration for Large-Scale 3D Environments Using Low-Memory Map," which tackles the memory and computational bottlenecks of onboard robot resources in vast, unknown spaces. With 4 citations, this work demonstrates Yan’s ability to design practical, resource-aware solutions for real-world deployment. Together, these contributions establish Yan as a promising researcher advancing the frontier of real-time spatial intelligence, enabling robots to perceive, map, and navigate complex environments with unprecedented efficiency and fidelity.
Research Focus
Key Achievements
Top Papers
- 1
- 2