用简单滤波器实现事件相机图像重建，实时高效且无需神经网络。
Filter-Based Reconstruction of Images from Events
- 采用IIR滤波器异步整合事件信号，实现低延迟重建。
- 通过检测静态像素并用高斯模糊降低噪声，提升图像质量。
- 适合对精度要求不高的实时任务，如标记物检测，代码开源。
从运动事件相机的事件中重建强度图像是一项挑战性任务，通常依赖部署在图形处理器上的神经网络。本文提出一种更简单的异步滤波重建方法（FIBAR）。首先，利用时序数字IIR滤波器对事件触发的强度变化进行积分；为减少重建噪声，提出一种新算法检测滞留像素，并调节最近更新像素的窗口。针对运动相机，认为某像素无事件意味着图像梯度较低，因此对滞留像素应用高斯模糊。与多数现有方法不同，FIBAR为异步运行，支持任意时间读取图像。其在现代笔记本CPU上以约4200万（无空间滤波）或1400万（启用空间滤波）事件/秒的速度运行。少量定性实验对比了FIBAR与基于神经网络的方法（FireNet）的重建效果：FIBAR重建图像噪声更高，存在鬼影现象，但对某些任务（如标记物检测）已足够。代码已公开于https://github.com/ros-event-camera/event_image_reconstruction_fibar。
原文摘要 · Abstract (English)
Reconstructing an intensity image from the events of a moving event camera is a challenging task that is typically approached with neural networks deployed on graphics processing units. This paper presents a much simpler, FIlter Based Asynchronous Reconstruction method (FIBAR). First, intensity changes signaled by events are integrated with a temporal digital IIR filter. To reduce reconstruction noise, stale pixels are detected by a novel algorithm that regulates a window of recently updated pixels. Arguing that for a moving camera, the absence of events at a pixel location likely implies a low image gradient, stale pixels are then blurred with a Gaussian filter. In contrast to most existing methods, FIBAR is asynchronous and permits image read-out at an arbitrary time. It runs on a modern laptop CPU at about 42(140) million events/s with (without) spatial filtering enabled. A few simple qualitative experiments are presented that show the difference in image reconstruction between FIBAR and a neural network-based approach (FireNet). FIBAR's reconstruction is noisier than neural network-based methods and suffers from ghost images. However, it is sufficient for certain tasks such as the detection of fiducial markers. Code is available at https://github.com/ros-event-camera/event_image_reconstruction_fibar
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