English

SCSim: A Realistic Spike Cameras Simulator

Computer Vision and Pattern Recognition 2024-05-28 v1

Abstract

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.

Keywords

Cite

@article{arxiv.2405.16790,
  title  = {SCSim: A Realistic Spike Cameras Simulator},
  author = {Liwen Hu and Lei Ma and Yijia Guo and Tiejun Huang},
  journal= {arXiv preprint arXiv:2405.16790},
  year   = {2024}
}

Comments

Accepted by ICME2024. arXiv admin note: substantial text overlap with arXiv:2304.03129

R2 v1 2026-06-28T16:41:14.800Z