Matterport3D 360° RGBD Dataset
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.
Cite this dataset as:
Rey-Area, M.,
Yuan, M.,
Richardt, C.,
2022.
Matterport3D 360° RGBD Dataset.
Bath: University of Bath Research Data Archive.
Available from: https://doi.org/10.15125/BATH-01126.
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Data
data_00.zip
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data_01.zip
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data_02.zip
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data_03.zip
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data_04.zip
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data_05.zip
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data_06.zip
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Creative Commons: Attribution 4.0
Creators
Manuel Rey-Area
University of Bath
Mingze Yuan
University of Bath
Christian Richardt
University of Bath
Contributors
University of Bath
Rights Holder
Matterport3D authors
Data Collector
Documentation
Methodology link:
Rey-Area, M., Yuan, M., and Richardt, C., 2021. 360MonoDepth: High-Resolution 360° Monocular Depth Estimation. Version 2. arXiv. Available from: https://doi.org/10.48550/ARXIV.2111.15669.
Documentation Files
README.md
text/plain (3kB)
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Funders
Engineering and Physical Sciences Research Council
https://doi.org/10.13039/501100000266
EPSRC Centre for Doctoral Training in Digital Entertainment
EP/L016540/1
Engineering and Physical Sciences Research Council
https://doi.org/10.13039/501100000266
Fellowship - Towards Immersive 360° VR Video with Motion Parallax
EP/S001050/1
Engineering and Physical Sciences Research Council
https://doi.org/10.13039/501100000266
Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) - 2.0
EP/T022523/1
Publication details
Publication date: 25 March 2022
by: University of Bath
Version: 1
DOI: https://doi.org/10.15125/BATH-01126
URL for this record: https://researchdata.bath.ac.uk/id/eprint/1126
Related papers and books
Rey-Area, M., Yuan, M., and Richardt, C., 2022. 360MonoDepth: High-Resolution 360° Monocular Depth Estimation. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. Available from: https://doi.org/10.1109/cvpr52688.2022.00374.
Related datasets and code
Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A., and Zhang, Y., n.d. Matterport3D. GitHub. Available from: https://github.com/niessner/Matterport.
Related online resources
Rey-Area, M., Yuan, M., and Richardt, C., 2022. 360MonoDepth: High-Resolution 360° Monocular Depth Estimation. GitHub. Available from: https://manurare.github.io/360monodepth/.
Contact information
Please contact the Research Data Service in the first instance for all matters concerning this item.
Contact person: Manuel Rey-Area
Faculty of Science
Computer Science
Research Centres & Institutes
Centre for Digital Entertainment (CDE)
Centre for the Analysis of Motion, Entertainment Research & Applications
