Awesome

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

This repository contains all the necessary bash scripts to download the dataset-

• To download the dataset you need to obtain a username and password. Please fill out the Google Form to request one.
• Update the assigned username password in the download scripts at lines: local uname=**** local pass=****
• To download the entire dataset at once (in the default directory $HOME/Downloads/doc3d), use the following command:
  • bash download_doc3d.sh
• To download in a specific directory-
  • bash download_doc3d.sh <out_dir>
• Individual bash scripts are provided to download a specific part of the data. Following will download all the image files in <out_dir>/doc3d/img/-
  • bash download_img.sh <out_dir>

Rendering codes are available!!: You can use the scripts here to render your own version of doc3D.

Some Notes:

• A download can be interrupted and resumed later, wget keeps track of it.
• Already downloaded files will be skipped and partially downloaded files will be resumed.
• The scripts are tested on Linux and Mac. For windows, a bash shell [probably-useful] should work.

Visualize Data:

Run the demo.py file to display a random image and corresponding ground-truths. demo.py takes the following flags-

• --data_root : Path to the doc3d dataset.
• --folder : Specific folder to load image from.
• --download_sample : If you want to download some samples and run demo.py on it. useful if you want to visualize it before downloading the entire data.
• --unwarp : Unwarp input image using the ground-truth backward mapping.

Release Updates:

• Sep 16, 2019: v0.5 (36K images, no depth map)
• Sep 17, 2019: v0.5.1 (Depth maps for v0.5 images)
• Sep 21, 2019: Rendering code is now available!
• Sep 22, 2019: v0.9 (65K images, no albedos)
• Mar 11, 2020: Please send an email to request the meshes (.obj)

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