This repository contains a PyTorch implementation of the paper:

PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flows.
Guandao Yang*, Xun Huang*, Zekun Hao, Ming-Yu Liu, Serge Belongie, Bharath Hariharan (* equal contribution)
ICCV 2019 (Oral)

As 3D point clouds become the representation of choice for multiple vision and graphics applications, the ability to synthesize or reconstruct high-resolution, high-fidelity point clouds becomes crucial. Despite the recent success of deep learning models in discriminative tasks of point clouds, generating point clouds remains challenging. This paper proposes a principled probabilistic framework to generate 3D point clouds by modeling them as a distribution of distributions. Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape. This formulation allows us to both sample shapes and sample an arbitrary number of points from a shape. Our generative model, named PointFlow, learns each level of the distribution with a continuous normalizing flow. The invertibility of normalizing flows enables computation of the likelihood during training and allows us to train our model in the variational inference framework. Empirically, we demonstrate that PointFlow achieves state-of-the-art performance in point cloud generation. We additionally show our model is able to faithfully reconstruct point clouds and learn useful representations in an unsupervised manner.

• Python 3.6
• CUDA 10.0.
• G++ or GCC 5.
• PyTorch. Codes are tested with version 1.0.1
• torchdiffeq.
• (Optional) Tensorboard for visualization of the training process.

Following is the suggested way to install these dependencies:

# Create a new conda environment
conda env create -f env.yaml
conda activate PointFlow 

# Compile structural losses, etc., but this step is not required 
# as there is a version of EMD/CD not requiring the CUDA kernel.
./install.sh

The point clouds are uniformly sampled from meshes from ShapeNetCore dataset (version 2) and use the official split. Please use this link to download the ShapeNet point cloud. The point cloud should be placed into data directory.

mv ShapeNetCore.v2.PC15k.zip data/
cd data
unzip ShapeNetCore.v2.PC15k.zip

Please contact us if you need point clouds for the ModelNet dataset.

Example training scripts can be found in scripts/ folder.

# Train auto-encoder (no latent CNF)
./scripts/shapenet_airplane_ae.sh # Train with single GPU, about 7-8 GB GPU memory
./scripts/shapenet_airplane_ae_dist.sh # Train with multiple GPUs

# Train generative model
./scripts/shapenet_airplane_gen.sh # Train with single GPU, about 7-8 GB GPU memory 
./scripts/shapenet_airplane_gen_dist.sh # Train with multiple GPUs 

Pretrained models can be downloaded from this link. The following is the suggested way to evaluate the performance of the pre-trained models.

unzip pretrained_models.zip;  # This will create a folder named pretrained_models

# Evaluate the reconstruction performance of an AE trained on the airplane category
CUDA_VISIBLE_DEVICES=0 ./scripts/shapenet_airplane_ae_test.sh; 

# Evaluate the reconstruction performance of an AE trained with the whole ShapeNet
CUDA_VISIBLE_DEVICES=0 ./scripts/shapenet_all_ae_test.sh;

# Evaluate the generative performance of PointFlow trained on the airplane category.
CUDA_VISIBLE_DEVICES=0 ./scripts/shapenet_airplane_gen_test.sh

The demo relies on Open3D. The following is the suggested way to install it:

conda install -c open3d-admin open3d 

The demo will sample shapes from a pre-trained model, save those shapes under the demo folder, and visualize those point clouds. Once this dependency is in place, you can use the following script to use the demo for the pre-trained model for airplanes:

CUDA_VISIBLE_DEVICES=0 ./scripts/shapenet_airplane_demo.sh

Please refer to the following github repository for our point cloud rendering code: https://github.com/zekunhao1995/PointFlowRenderer.

Please cite our work if you find it useful:

@article{pointflow,
 title={PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows},
 author={Yang, Guandao and Huang, Xun and Hao, Zekun and Liu, Ming-Yu and Belongie, Serge and Hariharan, Bharath},
 journal={arXiv},
 year={2019}
}