  Event sensors output a stream of asynchronous brightness changes (called
``events'') at a very high temporal rate. Previous works on recovering the lost
intensity information from the event sensor data have heavily relied on the
event stream, which makes the reconstructed images non-photorealistic and also
susceptible to noise in the event stream. We propose to reconstruct
photorealistic intensity images from a hybrid sensor consisting of a low frame
rate conventional camera, which has the scene texture information, along with
the event sensor. To accomplish our task, we warp the low frame rate intensity
images to temporally dense locations of the event data by estimating a
spatially dense scene depth and temporally dense sensor ego-motion. The results
obtained from our algorithm are more photorealistic compared to any of the
previous state-of-the-art algorithms. We also demonstrate our algorithm's
robustness to abrupt camera motion and noise in the event sensor data.
