Qingji Zeng

Papers

Total Citations

H-Index

About

Qingji Zeng is a leading researcher in autonomous driving perception and 3D semantic mapping, with a focus on real-time LiDAR-based environmental understanding. His most cited work, "Reconstruction of High-Precision Semantic Map" (2020, 5 citations), introduces a groundbreaking real-time Truncated Signed Distance Field (TSDF) approach that simultaneously achieves incremental surface reconstruction and highly accurate semantic segmentation from LiDAR point clouds. This innovation directly addresses the critical challenge of creating high-precision 3D semantic maps for autonomous navigation, enabling vehicles to not only perceive geometry but also understand object categories in real time. Zeng's contributions bridge the gap between traditional mapping and semantic understanding, offering a practical solution for robust perception in dynamic environments. His work has been recognized for its potential to advance autonomous systems, particularly in urban driving scenarios where accurate, real-time semantic reconstruction is essential. By integrating TSDF-based reconstruction with semantic segmentation, Zeng has laid the foundation for more intelligent and context-aware autonomous vehicles, making him a notable figure in the field of 3D computer vision and robotics.