Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset
This addresses the need for diverse training data in mobile photography applications, though it is incremental as it builds on existing dataset efforts.
The authors tackled the problem of limited stereo image datasets for computer vision by introducing Holopix50k, a large-scale in-the-wild dataset of 49,368 image pairs, which significantly improves results for tasks like stereo super-resolution and self-supervised monocular depth estimation.
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