Papers
8
Total Citations
158
H-Index
4
About
Xinming Huang is a leading researcher at the intersection of embedded systems, autonomous vehicle perception, and robotic surgery. His work fundamentally addresses the security and efficiency of autonomous systems, with his seminal 2013 paper on the security of autonomous systems employing embedded computing and sensors accumulating over 120 citations, establishing a critical foundation for the field. Huang has made major contributions to LiDAR perception, proposing a novel real-time fast channel clustering algorithm for point clouds and a distance transform pooling neural network for depth completion, both of which tackle the core challenge of sparse sensor data for autonomous navigation. His recent innovations extend to medical robotics, where he developed a cross-modality registration method using bone surface point clouds for robotic ultrasound-guided spine surgery, and to computer vision, with the RT-CBAM transformer for underwater image restoration. With a career spanning from early work on FlexRay communication protocols for automotive systems to cutting-edge transformer architectures, Huang’s research consistently bridges hardware-constrained embedded computing with advanced AI, driving safer and more capable autonomous and robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Security of Autonomous Systems Employing Embedded Computing and Sensors121 citations · 2013
- 2Real-Time Fast Channel Clustering for LiDAR Point Cloud13 citations · 2022
- 3Distance Transform Pooling Neural Network for LiDAR Depth Completion6 citations · 2021
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- 6Efficient sound source localization method using region selection4 citations · 2009
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