  We present EvDNeRF, a pipeline for generating event data and training an
event-based dynamic NeRF, for the purpose of faithfully reconstructing
eventstreams on scenes with rigid and non-rigid deformations that may be too
fast to capture with a standard camera. Event cameras register asynchronous
per-pixel brightness changes at MHz rates with high dynamic range, making them
ideal for observing fast motion with almost no motion blur. Neural radiance
fields (NeRFs) offer visual-quality geometric-based learnable rendering, but
prior work with events has only considered reconstruction of static scenes. Our
EvDNeRF can predict eventstreams of dynamic scenes from a static or moving
viewpoint between any desired timestamps, thereby allowing it to be used as an
event-based simulator for a given scene. We show that by training on varied
batch sizes of events, we can improve test-time predictions of events at fine
time resolutions, outperforming baselines that pair standard dynamic NeRFs with
event generators. We release our simulated and real datasets, as well as code
for multi-view event-based data generation and the training and evaluation of
EvDNeRF models (https://github.com/anish-bhattacharya/EvDNeRF).
