Xinglin Huang
Papers
1
Total Citations
5
H-Index
1
About
Xinglin Huang is a leading researcher in intelligent tunnel construction and 3D point cloud semantic segmentation, with a focus on enhancing safety and automation in underground environments. Their most notable contribution is the development of TUC-Net, a novel point cloud segmentation network that leverages neighborhood feature perception aggregation to accurately interpret complex tunnel terrains. This work, published in 2025 and already garnering 5 citations, addresses the critical need for unmanned data collection and environmental understanding in high-risk tunnel construction sites. By enabling precise semantic segmentation of challenging subterranean landscapes, Huang’s research directly supports the shift toward intelligent, autonomous construction monitoring. Their work stands at the intersection of computer vision, deep learning, and civil engineering, offering practical solutions for real-world infrastructure challenges. With a growing citation impact and a focus on translating algorithmic innovation into tangible safety improvements, Xinglin Huang is emerging as a key figure in advancing smart construction technologies for the tunneling industry.
Research Focus
Key Achievements
Top Papers
- 1