Monte Carlo Path Tracing and Statistical Event Detection for Event Camera Simulation
Yuichiro Manabe, Tatsuya Yatagawa, Shigeo Morishima, Hiroyuki Kubo

TL;DR
This paper introduces a physically accurate event camera simulation system using Monte Carlo path tracing with adaptive sampling based on statistical hypothesis testing, significantly improving efficiency and realism for computer vision research.
Contribution
It presents the first adaptive Monte Carlo path tracing method for simulating event cameras, utilizing statistical hypothesis testing to optimize sampling and improve simulation speed and accuracy.
Findings
Achieves significant speed-up over uniform sampling methods.
Models logarithmic luminance differences using normal distribution.
Provides a physically accurate simulation of event camera behavior.
Abstract
This paper presents a novel event camera simulation system fully based on physically based Monte Carlo path tracing with adaptive path sampling. The adaptive sampling performed in the proposed method is based on a statistical technique, hypothesis testing for the hypothesis whether the difference of logarithmic luminances at two distant periods is significantly larger than a predefined event threshold. To this end, our rendering system collects logarithmic luminances rather than raw luminance in contrast to the conventional rendering system imitating conventional RGB cameras. Then, based on the central limit theorem, we reasonably assume that the distribution of the population mean of logarithmic luminance can be modeled as a normal distribution, allowing us to model the distribution of the difference of logarithmic luminance as a normal distribution. Then, using Student's t-test, we…
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Taxonomy
TopicsDigital Radiography and Breast Imaging · Age of Information Optimization
MethodsSparse Evolutionary Training · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
