Flynn and co's new approach is to train a machine vision algorithm to work out what the new image should look like having been trained on a vast dataset of sequential images.
They trained their algorithm using "images of street scenes captured by a moving vehicle." Indeed, they use 100,000 of these sequences as a training data set.
They then tested it by removing one frame from a sequence of Street View images and asking it to reproduce it by looking only at the other images in the sequence. Finally, they compare the synthesized image with the one that was removed, giving them a kind of gold standard to contrast it with.
The results are impressive. "Overall, our model produces plausible outputs that are difficult to immediately distinguish from the original imagery," say Flynn and co.
DeepStereo: Learning to Predict New Views from the World's Imagery