Papers
2
Total Citations
6
H-Index
2
About
Xinzhe Liu is a researcher advancing the frontiers of autonomous driving and robotic perception through innovative work in 3D object detection and stereo vision. His primary research areas encompass stereo matching algorithms, LiDAR-based 3D object detection, and efficient hardware implementation for intelligent systems. Liu’s notable contributions include the development of CLIF (Cross-Layer Information Fusion), a novel framework that enhances stereo matching accuracy by integrating multi-level feature representations, directly addressing the computational and precision challenges critical for real-time robotics and autonomous navigation. In the domain of 3D perception, he introduced PillarTsAE, a high-performance pillar-based network that significantly improves the detection of small objects in complex LiDAR point clouds while reducing computational overhead—a key bottleneck for practical deployment. Though early in his career, his work has already garnered citations (4 and 2 for his most-cited papers, respectively), reflecting growing recognition in the field. Liu’s research stands out for its dual focus on algorithmic innovation and hardware-aware design, bridging the gap between theoretical performance and real-world applicability. His contributions are particularly relevant for students and engineers seeking efficient, scalable solutions for next-generation autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2