Guoyan Huang
Papers
2
Total Citations
22
H-Index
2
About
Guoyan Huang is a rising researcher in computer vision, specializing in self-supervised monocular depth estimation—a critical technology for autonomous driving and robotics. His work addresses the challenge of inferring 3D depth from a single camera without labeled data, a task essential for real-world perception systems. Huang’s major contributions center on developing lightweight, real-time architectures that balance accuracy with computational efficiency. His most cited paper, "LDA-Mono: A lightweight dual aggregation network for self-supervised monocular depth estimation" (2024, 13 citations), introduces a novel dual aggregation mechanism that enhances depth prediction while maintaining a compact model size. Building on this, "RTIA-Mono: Real-time lightweight self-supervised monocular depth estimation with global-local information aggregation" (2024, 9 citations) further integrates global and local features for real-time performance. These works demonstrate Huang’s ability to push the boundaries of efficient deep learning, making depth estimation viable for resource-constrained platforms. His research has already garnered attention in the field, with citations growing rapidly. Huang’s achievements highlight his potential to shape the future of autonomous perception systems, offering practical solutions that bridge the gap between cutting-edge accuracy and real-world deployability.
Research Focus
Key Achievements
Top Papers
- 1
- 2