  This paper presents a novel method for labeling real-world neuromorphic
camera sensor data by calculating the likelihood of generating an event at each
pixel within a short time window, which we refer to as "event probability mask"
or EPM. Its applications include (i) objective benchmarking of event denoising
performance, (ii) training convolutional neural networks for noise removal
called "event denoising convolutional neural network" (EDnCNN), and (iii)
estimating internal neuromorphic camera parameters. We provide the first
dataset (DVSNOISE20) of real-world labeled neuromorphic camera events for noise
removal.
