  Autofocus is an important task for digital cameras, yet current approaches
often exhibit poor performance. We propose a learning-based approach to this
problem, and provide a realistic dataset of sufficient size for effective
learning. Our dataset is labeled with per-pixel depths obtained from multi-view
stereo, following "Learning single camera depth estimation using dual-pixels".
Using this dataset, we apply modern deep classification models and an ordinal
regression loss to obtain an efficient learning-based autofocus technique. We
demonstrate that our approach provides a significant improvement compared with
previous learned and non-learned methods: our model reduces the mean absolute
error by a factor of 3.6 over the best comparable baseline algorithm. Our
dataset and code are publicly available.
