# Monte Carlo Path Tracing and Statistical Event Detection for Event Camera Simulation

arXiv: https://arxiv.org/abs/2408.07996 · HTML: https://mlsanity.com/paper/2408.07996/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2007.15548/) (2020)
- [Semi-Dense 3D Reconstruction with a Stereo Event Camera](https://mlsanity.com/paper/1807.07429/) (2018)
- [EventGAN: Leveraging Large Scale Image Datasets for Event Cameras](https://mlsanity.com/paper/1912.01584/) (2019)
- [Differentiable Event Stream Simulator for Non-Rigid 3D Tracking](https://mlsanity.com/paper/2104.15139/) (2021)
- [Exploiting Alternating DVS Shot Noise Event Pair Statistics to Reduce Background Activity](https://mlsanity.com/paper/2304.03494/) (2023)
- [Event-based Camera Simulation using Monte Carlo Path Tracing with Adaptive Denoising](https://mlsanity.com/paper/2303.02608/) (2023)

## Built on by (most cited first, 1 of 1)

- [EVIS: Real-Time Event Camera Simulation with Multimodal Supervision in NVIDIA Isaac Sim](https://mlsanity.com/paper/2607.08098/) (2026)

## Abstract (arXiv)

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 can test the hypothesis and determine whether to discard the null hypothesis for event non-occurrence. When we sample a sufficiently large number of path samples to satisfy the central limit theorem and obtain a clean set of events, our method achieves significant speed up compared to a simple approach of sampling paths uniformly at every pixel. To our knowledge, we are the first to simulate the behavior of event cameras in a physically accurate manner using an adaptive sampling technique in Monte Carlo path tracing, and we believe this study will contribute to the development of computer vision applications using event cameras.