  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
https://github.com/BIT-Vision/SpikeNeRF.
