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Holopix50k
No Label
Super Resolution
|...
License: Custom

Overview

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.

Citation

Please use the following citation when referencing the dataset:

@InProceedings{hua2020holopix50k,
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}
}

License

Custom

Data Summary
Type
Image,
Amount
--
Size
23.36GB
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.
| Amount -- | Size 23.36GB
Holopix50k
No Label
Super Resolution
License: Custom

Overview

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.

Citation

Please use the following citation when referencing the dataset:

@InProceedings{hua2020holopix50k,
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}
}

License

Custom

0
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