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When a camera travels across a 3D world, only a fraction of pixel valuechanges; an event-based camera observes the change as sparse events. How can weutilize sparse events for efficient recovery of the camera pose? We show thatwe can recover the camera pose by minimizing the error between sparse eventsand the temporal gradient of the scene represented as a neural radiance field(NeRF). To enable the computation of the temporal gradient of the scene, weaugment NeRF's camera pose as a time function. When the input pose to the NeRFcoincides with the actual pose, the output of the temporal gradient of NeRFequals the observed intensity changes on the event's points. Using thisprinciple, we propose an event-based camera pose tracking framework calledTeGRA which realizes the pose update by using the sparse event's observation.To the best of our knowledge, this is the first camera pose estimationalgorithm using the scene's implicit representation and the sparse intensitychange from events.

Event-based Camera Tracker by $\nabla$t NeRF