Kaiyue Li

National University of Defense Technology

Papers

Total Citations

H-Index

About

Kaiyue Li is a researcher specializing in event-based vision and bio-inspired sensing technologies, with a particular focus on advancing perception capabilities for robotics in dynamic environments. Li's most notable contribution is the development of FA-Harris, a fast and asynchronous corner detection method designed specifically for event cameras — a cutting-edge class of neuromorphic sensors that capture per-pixel brightness changes with microsecond temporal resolution rather than traditional frame-based imaging. Introduced in 2019, this work addressed a critical challenge in event-based computing: performing reliable, low-latency feature detection without the computational overhead associated with conventional frame-based approaches. FA-Harris has accumulated over 56 citations, reflecting its meaningful impact within the robotics and computer vision communities, where event cameras are increasingly recognized as transformative tools for high-speed, low-power perception tasks. Li's research sits at an exciting intersection of neuromorphic engineering and real-time robotics, contributing foundational algorithmic tools that enable robots to better navigate and respond to fast-changing environments. This work positions Li as an emerging voice in the growing field of event-driven sensing and asynchronous vision processing.