Xiaoyu Che
Papers
1
Total Citations
6
H-Index
1
About
Xiaoyu Che’s research lies at the intersection of 3D computer vision and robotics, with a particular focus on advancing object detection from point cloud data. His most cited work, “Scale-Aware Attention-Based PillarsNet (SAPN) Based 3D Object Detection for Point Cloud” (2020), introduces a novel deep learning architecture that enhances the precision of 3D object localization. By integrating scale-aware mechanisms and attention modules into a pillars-based framework, Che’s method significantly improves detection accuracy for objects of varying sizes—a critical capability for real-world applications like autonomous driving, housekeeping robots, and autonomous navigation. This contribution addresses a key challenge in robotics: enabling machines to perceive their environment with the spatial detail needed for safe and efficient operation. Although his citation count is still growing, with 6 citations to date, the work represents a meaningful step toward more robust 3D perception systems. Che’s research is particularly valuable for students and engineers working on LiDAR-based perception, offering a practical approach to balancing computational efficiency with detection fidelity in dynamic, cluttered scenes.
Research Focus
Key Achievements
Top Papers
- 1