  Asynchronously operating event cameras find many applications due to their
high dynamic range, vanishingly low motion blur, low latency and low data
bandwidth. The field saw remarkable progress during the last few years, and
existing event-based 3D reconstruction approaches recover sparse point clouds
of the scene. However, such sparsity is a limiting factor in many cases,
especially in computer vision and graphics, that has not been addressed
satisfactorily so far. Accordingly, this paper proposes the first approach for
3D-consistent, dense and photorealistic novel view synthesis using just a
single colour event stream as input. At its core is a neural radiance field
trained entirely in a self-supervised manner from events while preserving the
original resolution of the colour event channels. Next, our ray sampling
strategy is tailored to events and allows for data-efficient training. At test,
our method produces results in the RGB space at unprecedented quality. We
evaluate our method qualitatively and numerically on several challenging
synthetic and real scenes and show that it produces significantly denser and
more visually appealing renderings than the existing methods. We also
demonstrate robustness in challenging scenarios with fast motion and under low
lighting conditions. We release the newly recorded dataset and our source code
to facilitate the research field, see https://4dqv.mpi-inf.mpg.de/EventNeRF.
