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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.


Please use the following citation when referencing the dataset:

author = {Yiwen Hua and Puneet Kohli and Pritish Uplavikar and Anand Ravi and Saravana Gunaseelan
and Jason Orozco and Edward Li},
title = {Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset},
booktitle = {CVPR Workshop on Computer Vision for Augmented
and Virtual Reality, Seattle, WA, 2020.},
month = {June},
year = {2020}



Data Summary
Provided by
Leia inc
Leia Inc. is a Menlo Park-based startup company developing display technology that uses nanostructures to diffract a backlight directionally into a light field and create "holographic" visual effects.
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