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.
@article{arxiv.2303.02608,
title = {Event-based Camera Simulation using Monte Carlo Path Tracing with Adaptive Denoising},
author = {Yuta Tsuji and Tatsuya Yatagawa and Hiroyuki Kubo and Shigeo Morishima},
journal= {arXiv preprint arXiv:2303.02608},
year = {2024}
}