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.

本文介绍了一个大规模 360 度视频数据集，包含各种环境和上下文，以及相机的姿态和深度图，证明了该数据集在单一图像深度估计和视图合成两个主要任务中的相关性。

360 度广域：深度预测与视角合成数据集

360 in the Wild: Dataset for Depth Prediction and View Synthesis

In various imaging problems, we only have access to compressed measurements
of the underlying signals, hindering most learning-based strategies which
usually require pairs of signals and associated measurements for training.
Learning only from compressed measurements is impossible in general, as the
compressed observations do not contain information outside the range of the
forward sensing operator. We propose a new end-to-end self-supervised framework
that overcomes this limitation by exploiting the equivariances present in
natural signals. Our proposed learning strategy performs as well as fully
supervised methods. Experiments demonstrate the potential of this framework on
inverse problems including sparse-view X-ray computed tomography on real
clinical data and image inpainting on natural images. Code has been made
available at: this https URL

提出了一种自我监督的框架，利用自然信号中的等变性来克服仅从压缩信号学习的难题，并成功地在多种成像问题上实现，包括稀疏视角 X 射线计算机断层扫描和自然图像修复