# EventFly: Event Camera Perception from Ground to the Sky Source: [EventFly: Event Camera Perception from Ground to the Sky]() Collection index: [llms.txt]() Data updated: 2026-01-02T09:29:38.913Z Authors: Lingdong Kong, Dongyue Lu, Xiang Xu, Lai Xing Ng, Wei Tsang Ooi, Benoit R. Cottereau Published: 2025-03-25T17:59:59.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on EXPo vehicle to drone 1.0 (test)](): Semantic segmentation; EXPo vehicle to drone 1.0 (test); Accuracy: 69.17. - [Semantic Segmentation on EXPo vehicle (Pv) to quadruped (Pq)](): Semantic segmentation; EXPo vehicle (Pv) to quadruped (Pq); Accuracy: 73.42. - [Semantic Segmentation on EXPo quadruped to vehicle v1.0](): Semantic segmentation; EXPo quadruped to vehicle v1.0; Accuracy: 73.93. - [Semantic Segmentation on EXPo Drone-to-Vehicle target v1 (test)](): Semantic segmentation; EXPo Drone-to-Vehicle target v1 (test); Acc: 75.5. - [Semantic Segmentation on EXPo Drone-to-Quadruped v1 (test)](): Semantic segmentation; EXPo Drone-to-Quadruped v1 (test); Accuracy: 69.68. - [Semantic Segmentation on EXPo drone to quadruped 1.0 (test)](): Semantic segmentation; EXPo drone to quadruped 1.0 (test); Accuracy (Overall): 69.68. - [Semantic Segmentation on EXPo Quadruped to Drone 1.0 (test)](): Semantic segmentation; EXPo Quadruped to Drone 1.0 (test); Accuracy: 65.78. - [Semantic Segmentation on EXPo Pv -\> Pd](): Semantic segmentation; EXPo Pv -\> Pd; Accuracy: 69.17. - [Semantic Segmentation on EXPo Pv -\> Pq](): Semantic segmentation; EXPo Pv -\> Pq; Acc: 73.42. - [Semantic Segmentation on EXPo Pd -\> Pv](): Semantic segmentation; EXPo Pd -\> Pv; Accuracy: 75.5. - [Semantic Segmentation on EXPo Pd -\> Pq](): Semantic segmentation; EXPo Pd -\> Pq; Acc: 73.93. - [Semantic Segmentation on EXPo Pq -\> Pd](): Semantic segmentation; EXPo Pq -\> Pd; Accuracy: 65.78. - [Semantic Segmentation on EXPo Average](): Semantic segmentation; EXPo Average; Accuracy: 71.25. - [Semantic Segmentation on EXPo Pq -\> Pv](): Semantic segmentation; EXPo Pq -\> Pv; Accuracy: 69.68. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()