360D

A dataset of omnidirectional (360 - spherical panoramas) images with their corresponding ground truth depths. The 360 dataset provides 360 color images of indoor scenes along with their corresponding ground truth depth annotations. This dataset is composed from renders of other publicly available textured 3D datasets of indoor scenes. Specifically, it contains renders from two Computer Generated (CG) datasets, SunCG, SceneNet, and two realistic ones, acquired by scanning indoor building, Stanford2D3D, and Matterport3D. The 360 renders are produced by utilizing a path-tracing renderer and placing a spherical camera and a uniform light source at the same position in the scene. More information can be found @ http://vcl.iti.gr/360-dataset/Recent work on depth estimation up to now has only focused on projective images ignoring 360 content which is now increasingly and more easily produced. However, we show that monocular depth estimation models trained on traditional images produce sub-optimal results on omnidirectional images, showcasing the need for training directly on 360 datasets, which however, are hard to acquire. In this work, we circumvent the challenges associated to acquiring high quality 360 datasets with ground truth depth annotations, by re-using recently released large scale 3D datasets and re-purposing them to 360 via rendering. This dataset, which is considerably larger than similar projective datasets, is publicly offered to the community to enable future research in this direction. We use this dataset to learn in an end-to-end fashion the task of depth estimation from 360 images. We show promising results in our synthesized data as well as in unseen realistic images