ACM Transactions on Graphics (Proceedings of SIGGRAPH), July 2022.
Yingying Ren*
·
Uday Kusupati*
·
Julian Panetta
·
Florin Isvoranu
·
Davide Pellis
·
Tian Chen
·
Mark Pauly
*joint first author (order determined by coin toss)
This repository contains the source code and data for the paper Umbrella Meshes: Elastic Mechanisms for Freeform Shape Deployment, published at SIGGRAPH 2022.
Note: major refactoring is in progress! Please stay tuned for updated versions in Fall 2022!
The C++ code relies on boost
and cholmod/umfpack
. The design optimization part of the code also depend on the commercial
optimization package knitro
; these
will be omitted from the build if knitro
is not found.
You can install all the necessary dependencies except knitro
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 npm6
conda install python
A few more packages need to be installed on a fresh Ubuntu 19.04 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
# LibIGL/GLFW dependencies
sudo apt install libgl1-mesa-dev libxrandr-dev libxinerama-dev libxcursor-dev libxi-dev
# Dependencies (pybind11, jupyterlab/notebooks)
sudo apt install python3-pip npm
# Ubuntu 19.04 packages an older version of npm that is incompatible with its nodejs version...
sudo npm install npm@latest -g
Clone this repository recursively so that its submodules are also downloaded:
git clone --recursive https://github.com/EPFL-LGG/UmbrellaMesh
Build the C++ code and its Python bindings using cmake
and your favorite
build system. For example, with ninja
:
cd UmbrellaMesh
mkdir build && cd build
cmake .. -GNinja
ninja
The preferred way to interact with the code is in a Jupyter notebook, using the Python bindings. We highly 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
Launch Jupyter lab from the root python directory:
cd python
jupyter lab
There are four demos in python/live_demos
. Try them out!
If you use parts of this code for your own research, please consider citing our paper:
@article{RKP2022umbrellameshes,
author = {Ren, Yingying and Kusupati, Uday and Panetta, Julian and Isvoranu, Florin and Pellis, Davide and Chen, Tian and Pauly, Mark},
title = {Umbrella Meshes: Elastic Mechanisms for Freeform Shape Deployment},
year = {2022},
issue_date = {July 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {41},
number = {4},
issn = {0730-0301},
url = {https://doi.org/10.1145/3528223.3530089},
doi = {10.1145/3528223.3530089},
journal = {Transactions on Graphics (Proceedings of SIGGRAPH)},
month = {jul},
articleno = {152},
numpages = {15},
keywords = {deployable structure, computational design, numerical optimization, fabrication, physics-based simulation}
}