SCSim: A Realistic Spike Cameras Simulator
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909211184594944 |
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| author | Hu, Liwen Ma, Lei Guo, Yijia Huang, Tiejun |
| author_facet | Hu, Liwen Ma, Lei Guo, Yijia Huang, Tiejun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16790 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SCSim: A Realistic Spike Cameras Simulator Hu, Liwen Ma, Lei Guo, Yijia Huang, Tiejun Computer Vision and Pattern Recognition 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. |
| title | SCSim: A Realistic Spike Cameras Simulator |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.16790 |