Inflatables

This is the codebase for our Siggraph paper, Computational Inverse Design of Surface-based Inflatables. The code is written primarily in C++, but it is meant to be used through the Python bindings.

Getting Started

C++ Code Dependencies

The C++ code relies on Boost and CHOLMOD/UMFPACK, which must be installed separately.

The code also relies on several dependencies that are included as submodules: MeshFEM, libigl,

Finally, it includes a version of Keenan Crane's stripe patterns code modified to generate fusing curve patterns and fix a few issues with boundary handling.

macOS

You can install all the mandatory dependencies on macOS with MacPorts.

# Build/version control tools, C++ code dependencies
sudo port install cmake boost suitesparse ninja
# Dependencies for jupyterlab/notebooks
sudo port install python39
sudo port install npm7

Ubuntu 20.04

A few more packages need to be installed on a fresh Ubuntu 20.04 install:

# Build/version control tools
sudo apt install git cmake ninja-build
# Dependencies for C++ code
sudo apt install libboost-filesystem-dev libboost-system-dev libboost-program-options-dev libsuitesparse-dev
# Dependencies (pybind11, jupyterlab/notebooks)
sudo apt install python3-pip npm
sudo npm install npm@latest -g

Obtaining and Building

Clone this repository recursively so that its submodules are also downloaded:

git clone --recursive https://github.com/jpanetta/Inflatables

Build the C++ code and its Python bindings using cmake and your favorite build system. For example, with ninja:

cd Inflatables
mkdir build && cd build
cmake .. -GNinja
ninja

Running the Jupyter Notebooks

The preferred way to interact with the inflatables code is in a Jupyter notebook, using the Python bindings. We recommend that you install the Python dependencies and JupyterLab itself in a virtual environment (e.g., with venv).

pip3 install wheel # Needed if installing in a virtual environment
pip3 install jupyterlab ipykernel==5.5.5 # Use a slightly older version of ipykernel to avoid cluttering notebook with stdout content.
# If necessary, follow the instructions in the warnings to add the Python user
# bin directory (containing the 'jupyter' binary) to your PATH...

git clone https://github.com/jpanetta/pythreejs
cd pythreejs
pip3 install -e .
cd js
jupyter labextension install .

pip3 install matplotlib scipy
pip3 install shapely # dependency of the fabrication file generation

Launch Jupyter lab from the root python directory:

cd python
jupyter lab

Now try opening and running an demo notebook, e.g., python/Demos/ConcentricCircles.ipynb.

For an example of the full inverse design pipeline--from input surface to fabrication file output--please see python/Demos/Lilium.ipynb.