For simulated event data generation, we use Kubric to simulate and render scenes from multiple viewpoints, Vid2E to generate events from images, and then slice the event streams into varying batch sizes.

For real event data generation, we collect multiview event data from a single event camera by rotating the scene and triggering a repeatable object motion. We then manually time-synchronize the eventstreams by looking for peaks in the timestamp-event histogram.

We find that by training EvDNeRF with varied batch sizes of events, we achieve better events reconstructions of fine details under novel viewpoints, timestamps, and camera motion.