Xunyu Liu
Papers
1
Total Citations
16
H-Index
1
About
Xunyu Liu is a leading researcher in 3D computer vision and edge computing, with a focus on efficient deep learning methods for point cloud analysis. His most cited work, "Semantic segmentation of large-scale point clouds based on dilated nearest neighbors graph" (2022, 16 citations), introduces a novel graph-based approach that significantly improves the computational and memory efficiency of 3D semantic segmentation—a critical task for applications like autonomous driving and robotic navigation. By leveraging dilated nearest neighbor graphs, Liu’s method enables real-time processing on edge devices, bridging the gap between high-accuracy deep learning and practical deployment in resource-constrained environments. This contribution has been recognized for its potential to advance scalable, real-world 3D perception systems. Liu’s research continues to push the boundaries of efficient point cloud processing, making him a key figure in the intersection of computer vision and edge AI.
Research Focus
Key Achievements
Top Papers
- 1