Spike cameras, leveraging spike-based integration sampling and high temporal
resolution, offer distinct advantages over standard cameras. However, existing
approaches reliant on spike cameras often assume optimal illumination, a
condition frequently unmet in real-world scenarios. To address this, we
introduce SpikeNeRF, the first work that derives a NeRF-based volumetric scene
representation from spike camera data. Our approach leverages NeRF's multi-view
consistency to establish robust self-supervision, effectively eliminating
erroneous measurements and uncovering coherent structures within exceedingly
noisy input amidst diverse real-world illumination scenarios. The framework
comprises two core elements: a spike generation model incorporating an
integrate-and-fire neuron layer and parameters accounting for non-idealities,
such as threshold variation, and a spike rendering loss capable of generalizing
across varying illumination conditions. We describe how to effectively optimize
neural radiance fields to render photorealistic novel views from the novel
continuous spike stream, demonstrating advantages over other vision sensors in
certain scenes. Empirical evaluations conducted on both real and novel
realistically simulated sequences affirm the efficacy of our methodology. The
dataset and source code are released at
this https URL

使用尖峰相机数据，利用 SpikeNeRF 从尖峰相机数据中得出了基于 NeRF 的体积场景表示，既能在真实世界中去除错误测量，又能揭示出在各种真实世界的照明情况下一贯的结构，从而在某些场景中展现出比其他视觉传感器更大的优势。