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This repository contains a PyTorch implementation of the paper:
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[PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flows](https://arxiv.org/abs/1906.12320).
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<br>
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[Guandao Yang*](http://www.guandaoyang.com),
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[Xun Huang*](http://www.cs.cornell.edu/~xhuang/),
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[Zekun Hao](http://www.cs.cornell.edu/~zekun/),
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[Ming-Yu Liu](http://mingyuliu.net/),
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[Serge Belongie](http://blogs.cornell.edu/techfaculty/serge-belongie/),
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[Bharath Hariharan](http://home.bharathh.info/)
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(* equal contribution)
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<br>
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ICCV 2019 (**Oral**)
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## Introduction
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* G++ or GCC 5.
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* [PyTorch](http://pytorch.org/). Codes are tested with version 1.0.1
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* [torchdiffeq](https://github.com/rtqichen/torchdiffeq).
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* (Optional) [Tensorboard](https://www.tensorflow.org/) for visualization of training process.
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* (Optional) [Tensorboard](https://www.tensorflow.org/) for visualization of the training process.
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Following is the suggested way to install these dependencies:
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```bash
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unzip ShapeNetCore.v2.PC15k.zip
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```
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Please contact us if you need point clouds for ModelNet dataset.
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Please contact us if you need point clouds for the ModelNet dataset.
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## Training
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## Pre-trained models and test
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Pretrained models can be downloaded from this [link](https://drive.google.com/file/d/1dcxjuuKiAXZxhiyWD_o_7Owx8Y3FbRHG/view?usp=sharing).
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Following is the suggested way to evaluate the performance of the pre-trained models.
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The following is the suggested way to evaluate the performance of the pre-trained models.
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```bash
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unzip pretrained_models.zip; # This will create a folder named pretrained_models
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## Demo
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The demo relies on [Open3D](http://www.open3d.org/). Following is the suggested way to install it:
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The demo relies on [Open3D](http://www.open3d.org/). The following is the suggested way to install it:
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```bash
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conda install -c open3d-admin open3d
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```
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