Yao Zhao

Papers

Total Citations

H-Index

About

Yao Zhao is a computer vision researcher whose work spans camera calibration, pose estimation, and medical image understanding. His most significant contribution is a comprehensive 2023 survey on deep learning approaches to camera calibration, which has garnered 26 citations and serves as an important reference for researchers navigating the rapidly evolving landscape of learning-based geometric vision. This work critically examines how neural networks are transforming traditionally laborious calibration pipelines, making them more scalable for real-world deployment in robotics and autonomous systems. Zhao has also made meaningful strides in surgical AI, with his 2022 paper on scene graph-guided transformers for surgical report generation earning 10 citations — demonstrating his ability to bridge computer vision with clinical applications. His more recent work on Str-L Pose (2024) introduces a dual-graph framework that integrates structural line features alongside traditional point matching for relative pose estimation, addressing longstanding robustness challenges in autonomous driving and robotics perception. Across these contributions, Zhao demonstrates a consistent focus on making geometric reasoning more reliable and practical, positioning him as a versatile researcher at the intersection of foundational computer vision and applied intelligent systems.