360 in the Wild: Dataset for Depth Prediction and View Synthesis
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Abstract (arXiv)
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 360circ 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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 42 of its 69 references that are in the map. First posted on arXiv 27 Jun 2024.
Builds on
42 of 69 references.
References · 42
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30 more
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