  When a camera travels across a 3D world, only a fraction of pixel value
changes; an event-based camera observes the change as sparse events. How can we
utilize sparse events for efficient recovery of the camera pose? We show that
we can recover the camera pose by minimizing the error between sparse events
and the temporal gradient of the scene represented as a neural radiance field
(NeRF). To enable the computation of the temporal gradient of the scene, we
augment NeRF's camera pose as a time function. When the input pose to the NeRF
coincides with the actual pose, the output of the temporal gradient of NeRF
equals the observed intensity changes on the event's points. Using this
principle, we propose an event-based camera pose tracking framework called
TeGRA which realizes the pose update by using the sparse event's observation.
To the best of our knowledge, this is the first camera pose estimation
algorithm using the scene's implicit representation and the sparse intensity
change from events.
