Adversarial-Surfaces-via-Band-Limited-Perturbations

This repository contains the majority of the code for Generating Adversarial Surfaces via Band-Limited Perturbations .

Dependencies

PyTorch 1.4

This project uses PyTorch in order to compute gradients and use accelerated hardware, hence a version of pytorch equal to 1.4 should be used.

Other packages

All other packages can be installed by running

pip install -r requirements.txt

CUDA KNN (optional)

In order to compute the nearest neighbors and chamfer distance, a CUDA implementation of KNN is used. It can be installed using

git clone https://github.com/unlimblue/KNN_CUDA.git
cd KNN_CUDA
make && make install

This dependency is necessary with some types of adversarial losses, otherwise it can be skipped.

Dataset Data

The data for the dataset can be downloaded at the following pages:

• FAUST
• CoMA (only the registered scan are required).
• SMAL (this was generated by us using the parameters available at http://smal.is.tue.mpg.de)
• SHREC14 (we used the real dataset in our experiments)

Once downloaded put the data inside a subfolder named raw inside a root folder choosen by you; e.g. if /home/faust is the root directory for the dataset, then the downloaded data should be put in /home/faust/raw).

Pre-trained Parameters

The pre-trained parametes for the classifiers can be found here. Pass this data in input when instantiating a classifier. For a more in detail explanation, look at this two tutorials:

• tutorial with ChebyNet
• tutorial with PointNet

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NOTE:

Unfortunately due to compatibility reasons, most of the code done on CoMA is not currently available. I will try to integrate it in the repository as soon as I have more time.

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