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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".

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.

# Move to src_dataset/
$ ./dataset_all.sh 

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

  • Generating a 3D shape
# 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.

# Move to src_rendering/
$ ./meshes_all.sh 

It requires a rendering process with Mitsuba renderer. 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}
}

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License

MIT License

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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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