Shouqian Chen
Papers
1
Total Citations
8
H-Index
1
About
Shouqian Chen is a leading researcher in computer vision and autonomous navigation, with a focus on monocular depth estimation and 3D scene understanding. His most cited work, "Superb Monocular Depth Estimation Based on Transfer Learning and Surface Normal Guidance" (2020, 8 citations), introduces a novel approach that leverages a lightweight convolutional neural network (CNN) for coarse depth prediction, enhanced by surface normal guidance and transfer learning. This method significantly improves the accuracy of 3D perception from single images—a critical capability for drones and robots executing path planning and navigation in complex environments. Chen’s contributions address the challenge of balancing computational efficiency with depth estimation precision, making his techniques particularly valuable for real-time, resource-constrained autonomous systems. His work has been recognized for advancing practical applications in robotics and unmanned aerial vehicles, where reliable scene understanding is essential. With a growing citation impact, Chen continues to influence the development of efficient, learning-based methods for spatial intelligence, bridging the gap between theoretical computer vision and real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1