  With the mass-market adoption of dual-camera mobile phones, leveraging stereo
information in computer vision has become increasingly important. Current
state-of-the-art methods utilize learning-based algorithms, where the amount
and quality of training samples heavily influence results. Existing stereo
image datasets are limited either in size or subject variety. Hence, algorithms
trained on such datasets do not generalize well to scenarios encountered in
mobile photography. We present Holopix50k, a novel in-the-wild stereo image
dataset, comprising 49,368 image pairs contributed by users of the Holopix
mobile social platform. In this work, we describe our data collection process
and statistically compare our dataset to other popular stereo datasets. We
experimentally show that using our dataset significantly improves results for
tasks such as stereo super-resolution and self-supervised monocular depth
estimation. Finally, we showcase practical applications of our dataset to
motivate novel works and use cases. The Holopix50k dataset is available at
http://github.com/leiainc/holopix50k
