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data archive 12266 disk0/00/00/11/26 2022-03-25 16:58:16 2026-04-04 04:54:37 2022-03-25 16:58:16 data_collection show Rey-Area Manuel mra59@bath.ac.uk 0000-0002-4090-0930 University of Bath TRUE Yuan Mingze yuanmingze2014@gmail.com 0000-0001-5403-5979 University of Bath FALSE Richardt Christian christian@richardt.name 0000-0001-6716-9845 University of Bath FALSE FB0040 FB0110 dept_compsci 360 Monocular Depth Estimation, Virtual Reality, 3D geometry This dataset is an extension of Matterport3D that contains data to train and validate high resolution 360 monocular depth estimation models. The data is structured in 90 folders belonging to 90 different buildings storing a total of 9684 samples. Each sample of the dataset consists of 4 files: the RGB equirectangular 360 image (.png), its depth ground-truth (.dpt), a visualisation of the depth ground-truth (.png) and the camera to world extrinsic parameters for the image (.txt) saved as 7 parameters: 3 for the camera center and the last 4 for the XYWZ rotation quaternion. 2022-03-25 University of Bath public RightsHolder University of Bath DataCollector Matterport3D authors Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/L016540/1 EPSRC Centre for Doctoral Training in Digital Entertainment Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/S001050/1 Fellowship - Towards Immersive 360° VR Video with Motion Parallax Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/T022523/1 Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) - 2.0 cent_dig_ent cent_camera https://doi.org/10.48550/arXiv.2111.15669 en 1 10.15125/BATH-01126 https://doi.org/10.1109/CVPR52688.2022.00374 pub https://github.com/niessner/Matterport data https://manurare.github.io/360monodepth/ website open