Yuxiao Huang
Papers
1
Total Citations
6
H-Index
1
About
Yuxiao Huang is a leading researcher in 3-D object detection for autonomous driving and intelligent transportation systems, with a focus on LiDAR-based perception. Their most-cited work, "HybridPillars: Hybrid Point-Pillar Network for Real-Time Two-Stage 3-D Object Detection" (2024, 6 citations), introduces a novel architecture that bridges the gap between speed and accuracy in real-time detection. By combining point-based and pillar-based feature extraction in a two-stage framework, Huang's approach enables precise pointwise refinement while maintaining computational efficiency—a critical advancement for deployment in autonomous vehicles. This work addresses the longstanding challenge of balancing detection accuracy with real-time performance, contributing to safer and more reliable perception systems. Huang's research has quickly gained traction in the autonomous driving community, with their method offering a practical solution for integrating high-precision 3-D object detection into resource-constrained environments. Their contributions are particularly notable for advancing point-voxel hybrid methods, paving the way for next-generation perception systems that require both speed and accuracy in dynamic real-world scenarios.
Research Focus
Key Achievements
Top Papers
- 1