  We present Ev-NeRF, a Neural Radiance Field derived from event data. While
event cameras can measure subtle brightness changes in high frame rates, the
measurements in low lighting or extreme motion suffer from significant domain
discrepancy with complex noise. As a result, the performance of event-based
vision tasks does not transfer to challenging environments, where the event
cameras are expected to thrive over normal cameras. We find that the multi-view
consistency of NeRF provides a powerful self-supervision signal for eliminating
the spurious measurements and extracting the consistent underlying structure
despite highly noisy input. Instead of posed images of the original NeRF, the
input to Ev-NeRF is the event measurements accompanied by the movements of the
sensors. Using the loss function that reflects the measurement model of the
sensor, Ev-NeRF creates an integrated neural volume that summarizes the
unstructured and sparse data points captured for about 2-4 seconds. The
generated neural volume can also produce intensity images from novel views with
reasonable depth estimates, which can serve as a high-quality input to various
vision-based tasks. Our results show that Ev-NeRF achieves competitive
performance for intensity image reconstruction under extreme noise conditions
and high-dynamic-range imaging.
