Filter-Based Reconstruction of Images from Events
This work addresses image reconstruction for event cameras, offering a fast, asynchronous alternative to GPU-based neural networks, but it is incremental as it focuses on simplicity and speed over quality.
The paper tackles the problem of reconstructing intensity images from event camera data by proposing FIBAR, a simpler filter-based method that runs on a CPU at up to 140 million events per second, though it produces noisier images with ghost artifacts compared to neural network approaches.
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