Official code for the paper "Deep Learning on Implicit Neural Representations of Shapes", published
at ICLR 2023.
Authors: Luca De Luigi*, Adriano Cardace*, Riccardo Spezialetti*, Pierluigi Zama Ramirez, Samuele
Salti, Luigi Di Stefano.
* joint first authorship
The code contained in this repository has been tested on Ubuntu 20.04 with Python 3.8.6.
Create a virtual environment and install the library pycarus:
$ python3 -m venv .venv
$ source .venv/bin/activate
$ pip install -U pip setuptools
$ pip install pycarus
Then, try to import pycarus to get the command that you can run to install all the needed Pytorch libraries:
$ python3
>>> import pycarus
...
ModuleNotFoundError: PyTorch is not installed. Install it by running: source /XXX/.venv/lib/python3.8/site-packages/pycarus/install_torch.sh
In this example, you can install all the needed Pytorch libraries by running:
$ source /XXX/.venv/lib/python3.8/site-packages/pycarus/install_torch.sh
This script downloads and installs the wheels for torch, torchvision, pytorch3d and torch-geometric.
Occasionally, it may fails due to pytorch3d wheel not being available anymore. If that happens,
please let us know or try to install pytorch3d manually.
Finally install the other dependencies:
$ pip install hesiod torchmetrics wandb h5py==3.0.0
The code for each experiment has been organized in a separate directory, containing also a README file with all the instructions.
Please contact us if you need access to the datasets used in the experiments, both the ones containing the raw 3D shapes and the ones with the INRs.
If you find our work useful, please cite us:
@inproceedings{deluigi2023inr2vec,
title = {Deep Learning on Implicit Neural Representations of Shapes},
author = {De Luigi, Luca
and Cardace, Adriano
and Spezialetti, Riccardo
and Zama Ramirez, Pierluigi
and Salti, Samuele
and Di Stefano, Luigi},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2023}
}