Table of Content

• Energy-Based Sliced Wasserstein distance
  • Getting Started
    • Datasets
      • ShapeNet Core with 55 categories (refered from FoldingNet.)
      • ModelNet40
      • ShapeNet Chair
      • 3DMatch
    • Installation
    • For docker users
  • Experiments
    • Point-cloud reconstruction
  • Acknowledgment

Getting Started

Datasets

ShapeNet Core with 55 categories (refered from FoldingNet.)

  cd dataset
  bash download_shapenet_core55_catagories.sh

ModelNet40

  cd dataset
  bash download_modelnet40_same_with_pointnet.sh

ShapeNet Chair

  cd dataset
  bash download_shapenet_chair.sh

3DMatch

  cd dataset
  bash download_3dmatch.sh

Installation

The code has been tested with Python 3.6.9, PyTorch 1.2.0, CUDA 10.0 on Ubuntu 18.04.

To install the required python packages, run

pip install -r requirements.txt

To compile CUDA kernel for CD/EMD loss:

cd metrics_from_point_flow/pytorch_structural_losses/
make clean
make

For docker users

For building the docker image simply run the following command in the root directory

docker build -f Dockerfile -t <tag> .

Experiments

Point-cloud reconstruction

Available arguments for training an autoencoder

train.py [-h] [--config CONFIG] [--logdir LOGDIR]
                [--data_path DATA_PATH] [--loss LOSS]
                [--autoencoder AUTOENCODER]

optional arguments:
  -h, --help                  show this help message and exit
  --config CONFIG             path to json config file
  --logdir LOGDIR             path to the log directory
  --data_path DATA_PATH       path to data for training
  --loss LOSS                 loss function. One of [swd, msw, vsw, ebsw]
  --autoencoder AUTOENCODER   model name. One of [pointnet]
  --f_type                    energy function type [exp, identity]
  --estimation_type           estimation of EBSWprivate [IS,SIR,IMH,RMH]
  --gradient_type             gradient estimator [independent (copy), normal (reinforce)]
  --inter_dim                 dimension of keys
  --proj_dim                  projected dimension in linformer
  --kappa                     scale of vMF distribution

Example

python3 train.py --config="config.json" \
                --logdir="logs/" \
                --data_path="dataset/shapenet_core55/shapenet57448xyzonly.npz" \
                --loss="swd" \
                --autoencoder="pointnet"

# or in short, you can run
bash train.sh

To test reconstruction

python3 reconstruction/reconstruction_test.py  --config="reconstruction/config.json" \
                                              --logdir="logs/" \
                                              --data_path="dataset/modelnet40_ply_hdf5_2048/"

# or in short, you can run
bash reconstruction/test.sh

To render input and reconstructed point-clouds, please follow the instruction in render/README.md to install dependencies. To reproduce Figure 4 in our paper, run the following commands after training all autoencoders

python3 save_point_clouds.py
cd render
bash render_reconstruction.sh
cd ..
python3 concat_reconstructed_images.py

Acknowledgment

The structure of this repo is largely based on PointSWD. The structure of folder render is largely based on Mitsuba2PointCloudRenderer. We are very grateful for their open sources.