分离时空噪声机制，用轻量模型实现毫秒级事件去噪。
EDmamba: Rethinking Efficient Event Denoising with Spatiotemporal Decoupled SSMs
- 分空间与时间两个分支独立处理噪声，避免复杂联合建模。
- 仅88.9K参数、2.27GFLOPs，单卡68ms处理10万事件。
- 在4个公开数据集上超越现有最佳模型2.1个百分点。
事件相机具备微秒级延迟和宽动态范围，但原始数据常受空间伪影（如热点像素）和时间上不一致的背景活动干扰。现有方法联合处理整个4维事件体（x, y, p, t），导致计算开销大、延迟高。本文提出EDmamba，其核心思想是：空间与时间噪声源于不同物理机制，可独立抑制。首先通过极性与几何感知编码器提取粗粒度特征，再分别送入两个轻量状态空间分支：空间SSM学习位置相关滤波器以消除恒定伪影，时序SSM建模因果信号动态以去除突发背景事件。该解耦设计使模型仅需88.9K参数和2.27GFLOPs，单卡68ms完成10万事件实时处理，较最新Transformer基线快36倍。尽管极简，仍于四个公开基准上达到新最优，性能领先最强前代模型2.1个百分点。
原文摘要 · Abstract (English)
Event cameras provide micro-second latency and broad dynamic range, yet their raw streams are marred by spatial artifacts (e.g., hot pixels) and temporally inconsistent background activity. Existing methods jointly process the entire 4D event volume (x, y, p, t), forcing heavy spatio-temporal attention that inflates parameters, FLOPs, and latency. We introduce EDmamba, a compact event-denoising framework that embraces the key insight that spatial and temporal noise arise from different physical mechanisms and can therefore be suppressed independently. A polarity- and geometry-aware encoder first extracts coarse cues, which are then routed to two lightweight state-space branches: a Spatial-SSM that learns location-conditioned filters to silence persistent artifacts, and a Temporal-SSM that models causal signal dynamics to eliminate bursty background events. This decoupled design distills the network to only 88.9K parameters and 2.27GFLOPs, enabling real-time throughput of 100K events in 68ms on a single GPU, 36x faster than recent Transformer baselines. Despite its economy, EDmamba establishes new state-of-the-art accuracy on four public benchmarks, outscoring the strongest prior model by 2.1 percentage points.
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