Xiaoli Hao
Papers
1
Total Citations
99
H-Index
1
About
Xiaoli Hao is a leading researcher in computer vision and autonomous driving, with a primary focus on 3D object detection from LiDAR point clouds. Her most influential work, "SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection" (2019), has garnered 99 citations and introduced a novel approach that integrates shape attention mechanisms into regional proposal networks. This innovation significantly improves the accuracy of detecting objects in complex, real-world driving scenes by enabling the model to focus on geometric features of objects. Hao's contributions have advanced the field of perception for autonomous vehicles, addressing critical challenges in spatial understanding and sensor data interpretation. Her work is widely recognized for its practical impact, bridging the gap between deep learning research and real-time deployment in autonomous systems. With a citation count reflecting the relevance of her methods, Hao continues to influence the development of safer, more reliable self-driving technologies.
Research Focus
Key Achievements
Top Papers
- 1