Abstract
Event cameras capture scene changes asynchronously on a per-pixel basis, enabling extremely high temporal resolution. However, this advantage comes at the cost of high bandwidth, memory, and computational demands. To address this, prior work has explored event downsampling, but most approaches rely on fixed heuristics or threshold-based strategies, limiting their adaptability. Instead, we propose a probabilistic framework, POLED, that models event importance through an event-importance probability density function (ePDF), which can be arbitrarily defined and adapted to different applications. Our approach operates in a purely online setting, estimating event importance on-the-fly from raw event streams, enabling scene-specific adaptation. Additionally, we introduce zero-shot event downsampling, where downsampled events must remain usable for models trained on the original event stream, without task-specific adaptation. We design a contour-preserving ePDF that prioritizes structurally important events and evaluate our method across four datasets and tasks--object classification, image interpolation, surface normal estimation, and object detection--demonstrating that intelligent sampling is crucial for maintaining performance under event-budget constraints.
Abstract (translated)
事件相机以每像素异步方式捕捉场景变化，从而实现极高的时间分辨率。然而，这种优势也带来了高带宽、内存和计算需求的代价。为了解决这个问题，先前的研究探讨了事件降采样的方法，但大多数方法依赖于固定的启发式或基于阈值的策略，这限制了它们的适应性。为此，我们提出了一种概率框架POLED，通过定义事件重要性的概率密度函数（ePDF）来建模事件的重要性，该函数可以任意定义并根据不同的应用场景进行调整。我们的方法在完全在线环境中运行，直接从原始事件流中实时估计事件的重要性，从而实现针对特定场景的适应性。 此外，我们引入了无损事件降采样概念，在这种情况下，经过降采样的事件必须保持可用状态，以用于训练过程中使用完整事件流所得到的任务模型。为此，我们设计了一个边缘保留的ePDF来优先处理结构上重要的事件，并在四个数据集和任务（物体分类、图像插值、表面法线估计及物体检测）中评估了我们的方法，证明了智能采样对于在事件预算限制下保持性能至关重要。
URL
https://arxiv.org/abs/2506.02547
