Xinhai Li
Papers
1
Total Citations
3
H-Index
1
About
Xinhai Li is a leading researcher in computer vision and edge computing, with a primary focus on advancing stereo vision technology for real-time, energy-efficient applications. His most-cited work, "Real-Time Stereo Vision Hardware Accelerator: Fusion of SAD and Adaptive Census Algorithm" (2024, 3 citations), introduces a novel hardware accelerator that fuses sum of absolute differences (SAD) and adaptive census transform algorithms. This contribution directly addresses the critical challenge of achieving embodied intelligence on edge platforms—balancing power consumption, real-time processing, and accuracy for autonomous driving, robot navigation, and 3D reconstruction. By optimizing stereo matching for hardware implementation, Li’s work enables high-performance vision systems that operate within strict energy constraints, a key bottleneck in deploying AI on mobile and embedded devices. His research bridges the gap between algorithmic innovation and practical hardware deployment, making him a notable figure in the push toward efficient, real-world computer vision systems. Li’s contributions are foundational for next-generation autonomous systems that require both speed and low power consumption.
Research Focus
Key Achievements
Top Papers
- 1