|Description (include details on usage, files and paper references)||3D60 is a collective dataset generated in the context of various 360 vision research works , , . It comprises multi-modal stereo renders of scenes from realistic and synthetic large-scale 3D datasets (Matterport3D , Stanford2D3D , SunCG ).
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. Our 3D60 dataset fills a very important gap in data-driven spherical 3D vision and, more specifically, for monocular and stereo dense depth and surface estimation. 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.
We offer 3 different modalities (color images, depth and normal maps) and 3 viewpoints that comprise two stereo placements, horizontal and vertical.
 Zioulis, N.*, Karakottas, A.*, Zarpalas, D., and Daras, P. (2018). Omnidepth: Dense depth estimation for indoors spherical panoramas. In Proceedings of the European Conference on Computer Vision (ECCV).
 Zioulis, N., Karakottas, A., Zarpalas, D., Alvarez, F., and Daras, P. (2019). Spherical View Synthesis for Self-Supervised 360o Depth Estimation. In Proceedings of the International Conference on 3D Vision (3DV).
 Karakottas, A., Zioulis, N., Samaras, S., Ataloglou, D., Gkitsas, V., Zarpalas, D., and Daras, P. (2019). 360o Surface Regression with a Hyper-sphere Loss. In Proceedings of the International Conference on 3D Vision (3DV).
 Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A. and Zhang, Y. (2017). Matterport3d: Learning from rgb-d data in indoor environments. In Proceedings of the International Conference on 3D Vision (3DV).
 Armeni, I., Sax, S., Zamir, A.R. and Savarese, S., 2017. Joint 2d-3d-semantic data for indoor scene understanding. arXiv preprint arXiv:1702.01105.
 Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M. and Funkhouser, T., 2017. Semantic scene completion from a single depth image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).