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--- |
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license: mit |
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task_categories: |
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- image-to-image |
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--- |
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# doc3D |
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Doc3D is the first 3D dataset focused on document unwarping with realistic paper warping and renderings. |
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<p align="center"> |
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<img src="data.gif"> |
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</p> |
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It contains 100k images with the following ground-truths: |
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- 3D Coordinates |
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- Depth |
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- UV |
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- Backward Mapping |
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- Albedo |
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- Normals |
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- Checkerboard |
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### Useful links: |
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* More details of the data usage instructions are available in the GitHub repo: |
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https://github.com/cvlab-stonybrook/doc3D-dataset |
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* Link to the training code: https://github.com/cvlab-stonybrook/DewarpNet |
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* Link to the data generation code: https://github.com/sagniklp/doc3D-renderer |
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### Citation: |
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If you use the dataset, please consider citing our work- |
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``` |
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@inproceedings{SagnikKeICCV2019, |
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Author = {Sagnik Das*, Ke Ma*, Zhixin Shu, Dimitris Samaras, Roy Shilkrot}, |
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Booktitle = {Proceedings of International Conference on Computer Vision}, |
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Title = {DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks}, |
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Year = {2019}} |
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``` |
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#### Acknowlegement: |
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- Bash scripts are adapted from [epic-kitchens-download-scripts](https://github.com/epic-kitchens/download-scripts). |
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- Textures are obtained from: |
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- [Yes! Magazine](https://issues.yesmagazine.org/) under Creative Commons Licence. |
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- [CVF Open Access](http://openaccess.thecvf.com/menu.py) |
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- From books available under [Project Gutenberg](https://www.gutenberg.org/) |
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--- |
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license: mit |
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--- |
