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 simulators. We release our simulated and real datasets, as well as code
for both event-based data generation and the training of event-based dynamic
NeRF models (this https URL).

用于捕捉快速运动的刚性和非刚性变形的事件流的准确重建的管道的 EvDNeRF，可以从静态或移动视点在任何所需的时间戳之间预测动态场景的事件流，因此可用作给定场景的基于事件的模拟器。