Datasets:

Modalities:
Image
Languages:
English
Tags:
3D
License:

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

The Structured3D-Architecture repository contains parametric representation of the architecture from Structured3D. The full Structured3D consists of panoramic images together with annotated primitives of the 3D structure and their relationships. Please refer to the Structure3D website for downloading and agreeing to the terms of use of the original Structure3D data.
To request access to the Structured3D-Architecture, you will need to provide your full name (please provide both your first and last name), the name of your advisor or the principal investigator (PI) of your lab (in the PI/Advisor) fields, and the school or company that you are affiliated with (the Affiliation field). After requesting access to this repo, you will be considered for access approval.
In requesting access to the repository, you agree to 1) use the data only for non-commercial research and educational purposes. and 2) that you will not redistribute the downloaded data.

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We provide parametric architectures for use with SSTK extracted from Structure3D.

|-- arch                    # JSON files specifying the [architecture](https://github.com/smartscenes/sstk/wiki/Architecture-Format)  
|-- arch_renders            # Renderings of the architecture
    |-- roomId              # Renderings colored by roomId
        |-- images          # png images
        |-- camera_poses    # Information about the camera paramters used for the rendering
        |-- metadata        # CSV files specifying mapping of color to roomId
    |-- textured            # Renderings with texture

If you use this data, please cite the paper for the original Structure3D dataset from which this data was derived:

@inproceedings{zheng2020structured3d,
  title={Structured3d: A large photo-realistic dataset for structured 3d modeling},
  author={Zheng, Jia and Zhang, Junfei and Li, Jing and Tang, Rui and Gao, Shenghua and Zhou, Zihan},
  booktitle={Proceedings of the European Conference on Computer Vision},
  pages={519--535},
  year={2020},
  organization={Springer}
}
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