Ziyun Wang

Papers

Total Citations

H-Index

About

Ziyun Wang is a leading researcher in robotic perception, specializing in event-based vision, multi-sensor fusion, and high-speed dynamic environments. Their major contributions center on advancing the use of event cameras—bio-inspired sensors with microsecond-level latency and high dynamic range—for real-time robotics applications. Wang’s landmark work, "M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset" (2023, 50 citations), introduced the first comprehensive event camera dataset for robotics, featuring synchronized data from ground, legged, and aerial robots in challenging conditions. This resource has become essential for benchmarking perception algorithms in high-speed motion. Another key contribution, "EV-Catcher: High-Speed Object Catching Using Low-Latency Event-Based Neural Networks" (2022, 24 citations), demonstrated the practical power of event sensors by enabling a robotic system to catch fast-moving objects with unprecedented reliability, leveraging custom neural networks for real-time processing. Wang’s work bridges the gap between novel sensor hardware and deployable robotic intelligence, with clear impact in agile robotics, autonomous navigation, and industrial automation. Their datasets and methods are widely adopted by researchers pushing the boundaries of perception under extreme motion and lighting.