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UASOL, a large-scale high-resolution outdoor stereo dataset.

ABSTRACT: In this paper, we propose a new dataset for outdoor depth estimation from single and stereo RGB images. The dataset was acquired from the point of view of a pedestrian. Currently, the most novel approaches take advantage of deep learning-based techniques, which have proven to outperform traditional state-of-the-art computer vision methods. Nonetheless, these methods require large amounts of reliable ground-truth data. Despite there already existing several datasets that could be used for depth estimation, almost none of them are outdoor-oriented from an egocentric point of view. Our dataset introduces a large number of high-definition pairs of color frames and corresponding depth maps from a human perspective. In addition, the proposed dataset also features human interaction and great variability of data, as shown in this work.

SUBMITTER: Bauer Z

PROVIDER: S-EPMC6715739 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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UASOL, a large-scale high-resolution outdoor stereo dataset.

Bauer Zuria Z Gomez-Donoso Francisco F Cruz Edmanuel E Orts-Escolano Sergio S Cazorla Miguel M

Scientific data 20190829 1

In this paper, we propose a new dataset for outdoor depth estimation from single and stereo RGB images. The dataset was acquired from the point of view of a pedestrian. Currently, the most novel approaches take advantage of deep learning-based techniques, which have proven to outperform traditional state-of-the-art computer vision methods. Nonetheless, these methods require large amounts of reliable ground-truth data. Despite there already existing several datasets that could be used for depth e ...[more]

PMID: 31467361

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