  The large abundance of perspective camera datasets facilitated the emergence
of novel learning-based strategies for various tasks, such as camera
localization, single image depth estimation, or view synthesis. However,
panoramic or omnidirectional image datasets, including essential information,
such as pose and depth, are mostly made with synthetic scenes. In this work, we
introduce a large scale 360$^{\circ}$ videos dataset in the wild. This dataset
has been carefully scraped from the Internet and has been captured from various
locations worldwide. Hence, this dataset exhibits very diversified environments
(e.g., indoor and outdoor) and contexts (e.g., with and without moving
objects). Each of the 25K images constituting our dataset is provided with its
respective camera's pose and depth map. We illustrate the relevance of our
dataset for two main tasks, namely, single image depth estimation and view
synthesis.
