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StonyBrook-CVLab
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doc3D-dataset

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doc3D

Doc3D is the first 3D dataset focused on document unwarping with realistic paper warping and renderings.

It contains 100k images with the following ground-truths:

3D Coordinates
Depth
UV
Backward Mapping
Albedo
Normals
Checkerboard

Useful links:

More details of the data usage instructions are available in the GitHub repo: https://github.com/cvlab-stonybrook/doc3D-dataset
Link to the training code: https://github.com/cvlab-stonybrook/DewarpNet
Link to the data generation code: https://github.com/sagniklp/doc3D-renderer

Citation:

If you use the dataset, please consider citing our work-

@inproceedings{SagnikKeICCV2019, 
Author = {Sagnik Das*, Ke Ma*, Zhixin Shu, Dimitris Samaras, Roy Shilkrot}, 
Booktitle = {Proceedings of International Conference on Computer Vision}, 
Title = {DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks}, 
Year = {2019}}

Acknowlegement:

Bash scripts are adapted from epic-kitchens-download-scripts.
Textures are obtained from:
- Yes! Magazine under Creative Commons Licence.
- CVF Open Access
- From books available under Project Gutenberg

license: mit

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