Papers
2
Total Citations
50
H-Index
2
About
Peize Liu is a rising star in robotics and computer vision, whose work centers on real-time dense mapping and omnidirectional aerial perception. His major contributions include pioneering the use of Neural Radiance Fields (NeRF) for high-quality, real-time dense mapping, as demonstrated in his highly cited paper "H₂-Mapping: Real-Time Dense Mapping Using Hierarchical Hybrid Representation" (2023, 35 citations). This work addresses a critical challenge in robotics, AR/VR, and digital twins by enabling superior reconstruction quality while maintaining real-time performance. Liu also leads the development of "OmniNxt: A Fully Open-source and Compact Aerial Robot with Omnidirectional Visual Perception" (2024, 15 citations), which tackles the complexity of integrating omnidirectional cameras into aerial systems for inspection, reconstruction, and rescue tasks. By open-sourcing this compact platform, he has made advanced perception capabilities accessible to the broader research community. Liu’s work bridges the gap between theoretical advances in neural rendering and practical robotic systems, achieving notable impact in a short time. His research promises to accelerate the deployment of autonomous systems in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
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