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arXiv · 2303.02608

Event-based Camera Simulation using Monte Carlo Path Tracing with Adaptive Denoising

Yuta Tsuji·Tatsuya Yatagawa·Hiroyuki Kubo·Shigeo Morishima
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Abstract

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.

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Yuta Tsuji, Tatsuya Yatagawa, Hiroyuki Kubo, Shigeo Morishima. 2023-03-05. Event-based Camera Simulation using Monte Carlo Path Tracing with Adaptive Denoising. https://doi.org/10.1109/icip49359.2023.10222771

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