This synthetic event dataset is used in Robust e-NeRF to study the collective effect of camera speed profile, contrast threshold variation and refractory period on the quality of NeRF reconstruction from a moving event camera. It is simulated using an improved version of ESIM with three different camera configurations of increasing difficulty levels (i.e. easy, medium and hard) on seven Realistic Synthetic $360^{\circ}$ scenes (adopted in the synthetic experiments of NeRF), resulting in a total of 21 sequence recordings. Please refer to the Robust e-NeRF paper for more details.

The dataset allows for a retrospective comparison between event-based and image-based NeRF reconstruction methods, as the event sequences were simulated under highly similar conditions as the NeRF synthetic dataset. In particular, we adopt the same camera intrinsics and camera distance to the object at the origin. Furthermore, the event camera travels in a hemi-/spherical spiral motion about the object, thereby having a similar camera pose distribution for training. Apart from that, we also use the same test camera poses/views. Nonetheless, this new synthetic event dataset is not only specific to NeRF reconstruction, but also suitable for novel view synthesis, 3D reconstruction, localization and SLAM in general.