Spike-NeRF: Neural Radiance Field Based On Spike Camera
<|reference_start|>Spike-NeRF: Neural Radiance Field Based On Spike Camera: As a neuromorphic sensor with high temporal resolution, spike cameras offer notable advantages over traditional cameras in high-speed vision applications such as high-speed optical estimation, depth estimation, and object tracking. Inspired by the success of the spike camera, we proposed Spike-NeRF, the first Neural Radiance Field derived from spike data, to achieve 3D reconstruction and novel viewpoint synthesis of high-speed scenes. Instead of the multi-view images at the same time of NeRF, the inputs of Spike-NeRF are continuous spike streams captured by a moving spike camera in a very short time. To reconstruct a correct and stable 3D scene from high-frequency but unstable spike data, we devised spike masks along with a distinctive loss function. We evaluate our method qualitatively and numerically on several challenging synthetic scenes generated by blender with the spike camera simulator. Our results demonstrate that Spike-NeRF produces more visually appealing results than the existing methods and the baseline we proposed in high-speed scenes. Our code and data will be released soon.<|reference_end|>
@article{guo2024spike-nerf:,
    title={Spike-NeRF: Neural Radiance Field Based On Spike Camera},
    author={Yijia Guo, Yuanxi Bai, Liwen Hu, Mianzhi Liu, Ziyi Guo, Lei Ma, Tiejun
  Huang},
    journal={arXiv preprint arXiv:2403.16410},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16410},
    primaryClass={cs.CV}
}