  This paper presents an algorithm to obtain an event-based video from noisy
frames given by physics-based Monte Carlo path tracing over a synthetic 3D
scene. Given the nature of dynamic vision sensor (DVS), rendering event-based
video can be viewed as a process of detecting the changes from noisy brightness
values. We extend a denoising method based on a weighted local regression (WLR)
to detect the brightness changes rather than applying denoising to every pixel.
Specifically, we derive a threshold to determine the likelihood of event
occurrence and reduce the number of times to perform the regression. Our method
is robust to noisy video frames obtained from a few path-traced samples.
Despite its efficiency, our method performs comparably to or even better than
an approach that exhaustively denoises every frame.
