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https://github.com/postech-cvlab/combinatorial-3d-shape-generation

An official repository of paper "Combinatorial 3D Shape Generation via Sequential Assembly", presented at NeurIPS 2020 Workshop on Machine Learning for Engineering Modeling, Simulation, and Design
https://github.com/postech-cvlab/combinatorial-3d-shape-generation

3d-shape combinatorial-design dataset

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An official repository of paper "Combinatorial 3D Shape Generation via Sequential Assembly", presented at NeurIPS 2020 Workshop on Machine Learning for Engineering Modeling, Simulation, and Design

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# Combinatorial-3D-Shape-Generation

This is an official repository of paper "Combinatorial 3D Shape Generation via Sequential Assembly".

* arXiv Preprint: [(abs)](https://arxiv.org/abs/2004.07414), [(pdf)](https://arxiv.org/pdf/2004.07414.pdf)
* [High-Resolution Version](http://jungtaek.github.io/papers/ml4eng_2020_high_resolution.pdf) (About 50MB)

## Installing Required Python Packages (Python 3.7)

You are able to install required Python packages by commanding `pip install -r requirements.txt`.

## Running

* Creating a dataset

Run the following script.

```shell
# Move to src_dataset/
$ ./dataset_all.sh
```

It will create a dataset, which has already been included in the repository.

* Generating a 3D shape

```shell
# Move to src_generation/
$ python assemble_with_bo.py --ind_class 21 --ind_target 1 --use_stability --use_rollback
```

`ind_class` and `ind_target` indicate the indices of class and target object, respectively (Please check the code for dataset creation).
`use_stability` and `use_rollback` are flags for considering stability and using a rollback step.

* Creating an XML file and its corresponding PLY files

Run the following script.

```shell
# Move to src_rendering/
$ ./meshes_all.sh
```

It requires a rendering process with [Mitsuba renderer](http://mitsuba-renderer.org/index_old.html).
After changing the camera position and its perspective, render the XML file you want.

## Connection Types Between Two 2-by-4 Bricks















































## Examples in Combinatorial 3D Shape Dataset

* Bar





* Line





* Plate





* Wall





* Cuboid





* Square Pyramid





* Chair





* Sofa





* Cup





* Hollow





* Table





* Car





## Citation
```
@article{KimJ2020arxiv,
author={Kim, Jungtaek and Chung, Hyunsoo and Lee, Jinhwi and Cho, Minsu and Park, Jaesik},
title={Combinatorial {3D} Shape Generation via Sequential Assembly},
journal={{arXiv} preprint {arXiv}:2004.07414},
year={2020}
}
```

or

```
@inproceedings{KimJ2020neuripsw,
author={Kim, Jungtaek and Chung, Hyunsoo and Lee, Jinhwi and Cho, Minsu and Park, Jaesik},
title={Combinatorial {3D} Shape Generation via Sequential Assembly},
booktitle={NeurIPS Workshop on Machine Learning for Engineering Modeling, Simulation, and Design (ML4Eng)},
year={2020}
}
```

## Contributor
* Jungtaek Kim: [[email protected]](mailto:[email protected])
* Hyunsoo Chung: [[email protected]](mailto:[email protected])

## License
[MIT License](LICENSE)