重新思考高效和有效的基于点的网络,用于事件摄像头分类和回归
概括
EventMamba为事件摄像头数据提供了一种新的点云方法,在使用最小的计算资源的同时,在动作识别和重新定位任务中超越基于的方法.
科学领域:
- 计算机视觉 计算机视觉
- 神经形态工程的神经形态工程
- 机器学习 机器学习
背景情况:
- 事件摄像头提供低延迟和高动态范围,耗电最小.
- 目前基于框架的事件数据处理是计算密集的,并且失去时间细节.
- 现有的以点为基础的方法与时空事件流作斗争.
研究的目的:
- 开发一个高效和有效的框架,使用点云表示来处理事件摄像头数据.
- 解决基于框架和以前基于点的方法的局限性.
- 为了增强从事件流中提取时间信息.
主要方法:
- 提出了EventMamba,这是一个利用点云表示的框架,用于事件摄像头数据.
- 实现了层次结构,用于处理时间特征的分阶段模块.
- 重新设计全球提取器,使用时间聚合和基于状态空间模型 (SSM) 的Mamba进行增强的时间提取.
主要成果:
- 在六个行动识别数据集上实现了基于点的最先进 (SOTA) 性能.
- 在摄像头姿势转移 (CPR) 和眼睛追踪回归任务上表现优于所有基于的方法.
- 证明了最小的计算资源消耗.
结论:
- EventMamba有效地弥合了事件云和点云表示之间的差距.
- 提出的方法在捕获各种任务的时空信息方面表现出色.
- EventMamba 在高效和有效的事件摄像机数据处理方面取得了重大进展.
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