Kong, L., Lu, D., Xu, X., Ng, L. X., Ooi, W. T., & Cottereau, B. R. (2025). EventFly: Event Camera Perception from Ground to the Sky. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1472–1484. https://doi.org/10.1109/cvpr52734.2025.00145
Abstract:
Cross-platform adaptation in event-based dense perception is crucial for deploying event cameras across diverse settings, such as vehicles, drones, and quadrupeds, each with unique motion dynamics, viewpoints, and class distributions. In this work, we introduce EventFly, a framework for robust cross-platform adaptation in event camera perception. Our approach comprises three key components: i) Event Activation Prior (EAP), which identifies high-activation regions in the target domain to minimize prediction entropy, fostering confident, domain-adaptive predictions; ii) EventBlend, a data-mixing strategy that integrates source and target event voxel grids based on EAP-driven similarity and density maps, enhancing feature alignment; and iii) EventMatch, a dual-discriminator technique that aligns features from source, target, and blended domains for better domain-invariant learning. To holistically assess cross-platform adaptation abilities, we introduce EXPo, a large-scale benchmark with diverse samples across vehicle, drone, and quadruped platforms. Extensive experiments validate our effectiveness, demonstrating substantial gains over popular adaptation methods. We hope this work can pave the way for more adaptive, high-performing event perception across diverse and complex environments.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, - Prime Minister Office - CREATE – Intelligent Modelling for Decision-Making in Critical Urban Systems (DesCartes) program
Grant Reference no. : NA