Yuyan Li
Papers
2
Total Citations
16
H-Index
2
About
Yuyan Li’s research bridges computer vision and robotics, with a focus on omnidirectional perception and automated defect diagnosis. Their most influential work introduces **PanoDepth**, a novel two-stage pipeline for monocular omnidirectional depth estimation (2021, 14 citations). This model-agnostic framework addresses a critical challenge in 3D scene understanding, enabling applications in Virtual Reality, Autonomous Driving, and Robotics by extracting depth from a single 360° image. The approach is both efficient and adaptable, making it a valuable contribution to the field of spherical vision. In parallel, Li has advanced industrial inspection through **infrared image defect diagnosis** (2019, 2 citations), where they developed an automatic program using LAB color space transformation and image segmentation. This work reduces human labor in thermal diagnosis of power apparatus, demonstrating a practical impact on infrastructure maintenance. While early in their career, Li’s contributions are notable for their dual emphasis on theoretical depth estimation and applied defect detection, showcasing a versatility that promises further influence as their citation counts grow.
Research Focus
Key Achievements
Top Papers
- 1
- 2Infrared Image Defect Diagnosis through LAB Space Transformation2 citations · 2019