Xukun Shen
Papers
1
Total Citations
14
H-Index
1
About
Xukun Shen is a researcher specializing in 3D computer vision and efficient deep learning for large-scale point cloud analysis. His major contribution lies in advancing semantic segmentation of massive 3D scenes, particularly through his work on "ThickSeg," which introduces a multi-layer projection technique to balance accuracy and computational efficiency. This approach enables the processing of extensive point clouds—common in autonomous driving and urban mapping—without sacrificing performance. With 14 citations for this key paper, Shen’s work has already garnered attention from peers tackling similar scalability challenges. His research addresses a critical bottleneck in 3D perception: how to handle the sheer volume of data in real-world environments. By focusing on practical, deployable solutions, Shen is helping bridge the gap between theoretical models and real-time applications. For students and researchers exploring point cloud segmentation, his work offers a clear path toward handling large-scale data with limited resources.
Research Focus
Key Achievements
Top Papers
- 1