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.

使用高时间分辨率的脉冲相机，我们提出了 Spike-NeRF，一种基于脉冲数据的神经辐射场，用于高速场景的三维重建和新视点合成。通过设计特殊的脉冲掩蔽和损失函数，我们从高频但不稳定的脉冲数据中重建了准确且稳定的三维场景，并通过合成实验验证了其优于现有方法和基线模型在高速场景中的视觉效果。