Create your own version of the Inv3D dataset!
This repository contains the dataset generation code of our paper which has been accepted at the International Conference on Document Analysis and Recognition (ICDAR) 2023.
For more details see our project page project page.
For the dataset generation, your need the following ressources:
- Warped paper meshes (download samples orj full dataset)
- Company logos (download here)
- HDR envrionment maps (download samples or full dataset [currently offline, see issue])
- Fonts (already in this repository)
- Document templates (already in this repository)
All ressources must be placed in the corresponding asset folder within the top-level directory "assets". The asset integration can also be done using docker mounts to avoid copying these inside the container.
docker build -t inv3d-generator PATH_TO_REPOSITORY
docker run \
--cpus=8 -it \
--init \
--mount source=inv3d-volume,target=/usr/inv3d/out \
inv3d-generator \
--num_workers 4 \
--num_samples 10 \
default \
--resolution_rendering 448 \
--seed 42 \
--document_dpi 150
docker run \
--cpus=8 -it \
--init \
--mount source=inv3d-volume,target=/usr/inv3d/out \
--entrypoint python \
inv3d-generator \
-u src/resume.py --num_workers 4
If you use the code of our paper for scientific research, please consider citing
@article{hertlein2023inv3d,
title = {Inv3D: a high-resolution 3D invoice dataset for template-guided single-image document unwarping},
author = {Hertlein, Felix and Naumann, Alexander and Philipp, Patrick},
year = 2023,
journal = {International Journal on Document Analysis and Recognition (IJDAR)},
publisher = {Springer},
pages = {1--12}
}
This work is based on the dataset generation of Doc3D.
This project is licensed under MIT unless another license is specified in a given subfolder.