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Matterport3D 360° RGBD Dataset

  • Manuel Rey-Area (Creator)
  • Mingze Yuan (Creator)
  • Christian Richardt (Creator)

Dataset

Description

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.
Date made available25 Mar 2022
PublisherUniversity of Bath

Contact

  • 1 Chapter in a published conference proceeding

  • 360MonoDepth: High-Resolution 360° Monocular Depth Estimation

    Rey-Area, M., Yuan, M. & Richardt, C., 27 Sept 2022, Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022. IEEE, p. 3752-3762 11 p. 9879016. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 2022-June).

    Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

    Open Access
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    88   Link opens in a new tab Citations (SciVal)
    701 Downloads (Pure)

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