Modern 3D vision advancements rely on data driven methods and thus, task specific annotated datasets. Especially for geometric inference tasks like depth and surface estimation, the collection of high quality data is very challenging, expensive and laborious. While considerable efforts have been made for traditional pinhole cameras, the same cannot be said for omnidirectional ones. 3D60 is a collective dataset generated in the context of various 360o vision research works. It comprises multi-modal omnidirectional stereo renders of scenes from realistic and synthetic large-scale 3D datasets (Matterport3D, Stanford2D3D and SunCG). Our dataset fills a very important gap in data-driven spherical 3D vision and, more specifically, for the monocular and stereo dense depth and surface estimation tasks. We originate by exploiting the efforts made in providing synthetic and real scanned 3D datasets of interior spaces and re-using them via ray-tracing in order to generate high quality, densely annotated spherical panoramas.

Instructions, code and data splits available @ https://vcl3d.github.io/3D60/