Spike-NeRF: Neural Radiance Field Based On Spike Camera

• URL: http://arxiv.org/abs/2403.16410v1
• Date: Mon, 25 Mar 2024 04:05:23 GMT
• Title: Spike-NeRF: Neural Radiance Field Based On Spike Camera
• Authors: Yijia Guo, Yuanxi Bai, Liwen Hu, Mianzhi Liu, Ziyi Guo, Lei Ma, Tiejun Huang,
• Abstract summary: We propose Spike-NeRF, the first Neural Radiance Field derived from spike data. 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. Our results demonstrate that Spike-NeRF produces more visually appealing results than the existing methods and the baseline we proposed in high-speed scenes.
• Score: 24.829344089740303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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