Xing Jun Hu
Papers
1
Total Citations
6
H-Index
1
About
Xing Jun Hu has made impactful contributions to 3D object detection for autonomous driving and robotics, with a focus on balancing real-time performance with high accuracy. His key research areas include pillar-based 3D perception, quantization-aware feature encoding, and efficient onboard deployment of deep learning models. Hu’s most notable work, "PillarHist: A Quantization-aware Pillar Feature Encoder based on Height-aware Histogram" (2025), introduces a novel approach that enhances pillar-based detectors by incorporating height-aware histograms and quantization awareness, achieving state-of-the-art results while maintaining low computational overhead—critical for resource-constrained autonomous systems. This paper has already garnered 6 citations shortly after publication, reflecting its timely relevance. Hu’s research addresses a core challenge in autonomous driving: enabling real-time, high-performance detection without sacrificing accuracy. His work stands out for its practical focus on deployment efficiency, making it valuable for both academic researchers and industry engineers working on onboard perception systems. With a clear trajectory toward advancing efficient 3D vision, Hu is establishing himself as a promising contributor to the field.
Research Focus
Key Achievements
Top Papers
- 1