# SCSim: A Realistic Spike Cameras Simulator

arXiv: https://arxiv.org/abs/2405.16790 · HTML: https://mlsanity.com/paper/2405.16790/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 2 of 2)

- [A Retina-inspired Sampling Method for Visual Texture Reconstruction](https://mlsanity.com/paper/1907.08769/) (2019)
- [Optical Flow Estimation for Spiking Camera](https://mlsanity.com/paper/2110.03916/) (2021)

## Built on by (most cited first, 2 of 2)

- [SpikeGS: Reconstruct 3D scene via fast-moving bio-inspired sensors](https://mlsanity.com/paper/2407.03771/) (2024)
- [Seeing the Unseen in Low-light Spike Streams](https://mlsanity.com/paper/2509.23304/) (2025)

## Abstract (arXiv)

Spike cameras, with their exceptional temporal resolution, are revolutionizing high-speed visual applications. Large-scale synthetic datasets have significantly accelerated the development of these cameras, particularly in reconstruction and optical flow. However, current synthetic datasets for spike cameras lack sophistication. Addressing this gap, we introduce SCSim, a novel and more realistic spike camera simulator with a comprehensive noise model. SCSim is adept at autonomously generating driving scenarios and synthesizing corresponding spike streams. To enhance the fidelity of these streams, we've developed a comprehensive noise model tailored to the unique circuitry of spike cameras. Our evaluations demonstrate that SCSim outperforms existing simulation methods in generating authentic spike streams. Crucially, SCSim simplifies the creation of datasets, thereby greatly advancing spike-based visual tasks like reconstruction. Our project refers to https://github.com/Acnext/SCSim.