Xiangdong Zhang

Xidian University, State Nuclear Power Technology Company (China)

Papers

Total Citations

H-Index

About

Xiangdong Zhang is a researcher whose work bridges computer vision and cybersecurity, with a particular focus on scene understanding and automated text recognition. His most influential contribution, “Semantic scene completion with dense CRF from a single depth image” (2018), has garnered 33 citations and addresses a fundamental challenge in 3D vision: inferring the complete geometry and semantics of a scene from a single depth input. By integrating dense conditional random fields with deep learning, Zhang’s approach enables more accurate reconstruction of occluded or missing scene regions, advancing applications in robotics, autonomous navigation, and augmented reality. In parallel, Zhang explores the adversarial side of vision with his work on CAPTCHA recognition, where a Transformer-based model (2021) demonstrates how advanced neural architectures can break text-based security measures. This research not only highlights vulnerabilities in existing CAPTCHA systems but also informs the design of more robust human-verification mechanisms. Zhang’s dual focus—enhancing machine perception while probing its limits in security—positions him as a thoughtful contributor to both the capabilities and the safeguards of modern AI systems.