Neuromorphic vision sensors, such as the dynamic vision sensor (DVS) and spike camera, have gained increasing attention in recent years. The spike camera can detect fine textures by mimicking the fovea in the human visual system, and output a high-frequency spike stream. Real-time high-quality vision reconstruction from the spike stream can build a bridge to high-level vision task applications of the spike camera. To realize high-speed and high-quality vision reconstruction of the spike camera, we propose a new spike stability theorem that reveals the relationship between spike stream characteristics and stable light intensity. Based on the spike stability theorem, two parameter-free algorithms are designed for the real-time vision reconstruction of the spike camera. To demonstrate the performances of our algorithms, two datasets (a public dataset PKU-Spike-High-Speed and a newly constructed dataset SpikeCityPCL) are used to compare the reconstruction quality and speed of various reconstruction methods. Experimental results show that, compared with the current state-of-the-art (SOTA) reconstruction methods, our reconstruction methods obtain the best tradeoff between the reconstruction quality and speed. Additionally, we design the FPGA implementation method of our algorithms to realize the real-time (running at 20,000 FPS) visual reconstruction. Our work provides new theorem and algorithm foundations for the real-time edge-end vision processing of the spike camera.

翻译：近年来，神经形态视觉传感器（如动态视觉传感器（DVS）和脉冲相机）受到越来越多的关注。脉冲相机通过模仿人类视觉系统中的中央凹，能够检测精细纹理，并输出高频脉冲流。从脉冲流进行实时高质量的视觉重建，可以为脉冲相机的高层视觉任务应用搭建桥梁。为了实现脉冲相机的高速高质量视觉重建，我们提出了一种新的脉冲稳定定理，该定理揭示了脉冲流特性与稳定光强之间的关系。基于该脉冲稳定定理，我们设计了两种无参数算法，用于脉冲相机的实时视觉重建。为了验证我们算法的性能，我们使用了两个数据集（公开数据集PKU-Spike-High-Speed和新构建的数据集SpikeCityPCL）来比较各种重建方法的重建质量和速度。实验结果表明，与当前最先进（SOTA）的重建方法相比，我们的重建方法在重建质量和速度之间取得了最佳平衡。此外，我们设计了算法的FPGA实现方法，以实现实时（运行速度为20，000 FPS）视觉重建。我们的工作为脉冲相机的实时边缘端视觉处理提供了新的定理和算法基础。