Hua-Tsung Chen
Papers
1
Total Citations
3
H-Index
1
About
Hua-Tsung Chen is a leading researcher in computer vision and deep learning, with a primary focus on autonomous driving, robotics, and real-time visual perception. His work centers on developing efficient, lightweight neural networks for critical tasks such as monocular depth estimation and obstacle avoidance, enabling advanced environment sensing with minimal computational cost. Notably, his 2021 paper on "Real-time Monocular Depth Estimation with Extremely Light-Weight Neural Network" introduces a novel architecture that balances accuracy and speed, making it highly practical for resource-constrained platforms. Although a relatively recent contribution, this work has already garnered 3 citations, reflecting its growing influence in the field. Chen’s research addresses the pressing need for low-cost, camera-based solutions in autonomous systems, leveraging RGB sensors to extract rich depth information from single images. His contributions are particularly valuable for students and researchers interested in bridging the gap between theoretical deep learning models and real-world deployment in robotics and autonomous driving.
Research Focus
Key Achievements
Top Papers
- 1