发表机构
Istituto Italiano di Tecnologia（意大利理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对事件相机速度不变表示丢失时间信息的问题，提出SCARF，一种保留原始事件并统一处理快慢运动与静止场景的实时表示，兼具高效与高质量。
AI 中文摘要
事件相机为实时视觉任务（如本URL）提供低延迟、高时间分辨率的感知。实现这些优势的新型电路（即异步、独立像素）也引入了新的算法挑战。速度不变表示缓解了慢速运动下的观测缺失和快速运动下的运动模糊，但大多数通过将事件转换为图像类表示而丢弃了时间信息。我们提出了中心主动感受野集合（SCARF），一种实时的速度不变表示，它在保持原始事件的同时，一致地处理快速运动、静止场景和独立移动物体。SCARF在计算效率和表示质量方面均达到了最先进的性能。
英文摘要
Event cameras provide low-latency, high temporal resolution perception for real-time vision tasks such as robotics.The novel circuitry (i.e. asynchronous, independent pixels) that enables these advantages also introduces new algorithmic challenges. Velocity-invariant representations alleviate missing observations under slow motion and motion blur under fast motion, but most discard temporal information by converting events into image-like representations. We propose Set of Centre Active Receptive Fields (SCARF), a real-time velocity-invariant representation that preserves raw events while consistently handling fast motion, stationary scenes, and independently moving objects. SCARF achieves state-of-the-art performance in both computational efficiency and representation quality.
Comments@inproceedings{ikura2026event, title={An Event Preserving Velocity Invariant Representation for Event Cameras}, author={Ikura, Mikihiro and Gava, Luna and Wu, Jiahang and Glover, Arren and Bartolozzi, Chiara}, year={2026}, booktitle={ECCV 2026 Workshop-Event-Based Multimodal Vision: From Imaging to Perception and Understanding} }
Journal refIkura, M., Gava, L., Wu, J., Glover, A. and Bartolozzi, C., An Event Preserving Velocity Invariant Representation for Event Cameras. In ECCV 2026 Workshop-Event-Based Multimodal Vision: From Imaging to Perception and Understanding
