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.

Abstract

Design Type(s)	modeling and simulation objective • image creation and editing objective • database creation objective
Measurement Type(s)	image
Technology Type(s)	digital camera
Factor Type(s)	geographic location
Sample Characteristic(s)	Alicante • anthropogenic habitat

Machine-accessible metadata file describing the reported data (ISA-Tab format)