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Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification

This repository contains the official pytorch implementation for the paper "Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification"

Please access the project page for more details, datasets and pretrained checkpoints downloads: Project Page.

The tensorflow implementation (tf1.14) can be found at: Will Announce Soon.

Step to run

Download and Install

• Clone this repo.
• Download pre-processed dataset for ModelNet form the project page. Store it to data folder. (The urls are subject to change, please check the project page.)
• (Optional) If you want to use pretrained model, download checkpoints and store it to checkpoint folder.
• (Optional) Create an environment through conda by the provided environment.yml
  • You can also manually install the package:
    • Python 3.6-3.8, pytorch==1.8.0, pytorch-lightning==1.2.1, etc.
  • Typically, pytorch==1.8.0 require cuda >= 10.2. If you only have cuda 10.0 or 10.1. You may install pytorch==1.4.0
• (Optional) In order to calculate the quantitive result, compile extra pytorch operator. You can skip this.
  • see FAQ if error occured.

    # Clone package
    git clone git@github.com:fei960922/GPointNet.git
    cd GPointNet

    # Download dataset and checkpoint
    wget http://www.stat.ucla.edu/~jxie/GPointNet/data/modelnet_2k.zip 
    unzip -q modelnet_2k.zip 
    mkdir checkpoint
    wget http://www.stat.ucla.edu/~jxie/GPointNet/checkpoint/syn_cvpr_chair.ckpt -O checkpoint/syn_cvpr_chair.ckpt

    # Establish the environment and compile metrics.
    conda env create -f environment.yml 
    conda activate gpointnet_gpu
    cd metrics/pytorch_structural_losses
    make

Point Cloud Synthesis: Train from stratch

Please make sure you download the datasets.

python src/model_point_torch.py

By default, it run chair synthesis with default setting on a single GPU. It takes about 8 hours to train on Nvidia RTX2080 Ti.

Please check src/model_point_torch.py for argument details. If you have not compiled the metrics, please add -do_evaluation 0 to skip the evaluation.

Synthesis results from pretrained checkpoint

python tools/test_torch.py -category chair -checkpoint_path {path}.ckpt -synthesis

Add -reconstruction, -intepolation to perform reconstruction and intepolation. Add -evaluate to output quantitive result.

python tools/test_torch.py -category chair -checkpoint_path {path}.ckpt -synthesis -evaluate -reconstruction -intepolation

Do classification

To run classification, please download and compile Libsvm.

python tools/classification_torch.py -checkpoint_path output/checkpoint_default_big.ckpt

See tools/run_examples.sh for more examples.

FAQ

Common issue related to evaluation metric compile

• cd metrics/pytorch_structural_losses
• make, if failed:
  • Change c++11 to c++14 in Makefile:Line 69-70
  • Change nvcc path in Makefile:Line 9 to match current cuda version.
    • If install pytorch with conda, nvcc is not installed by default.
    • Install cuda: conda install -c conda-forge nvcc_linux-64=11.1 (11.1 is the cuda version)
    • If so, the nvcc is in ~/miniconda3/envs/{ENV}/bin/nvcc.
  • If error on ninja, change setup.py:Line 23 to cmdclass={'build_ext': BuildExtension.with_options(use_ninja=False)}
  • If compiled successfully but no file found, change Makefile:Line74-75 accordingly. mv mean move files.

Reference

@inproceedings{GPointNet,
    title={Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification},
    author={Xie, Jianwen and Xu, Yifei and Zheng, Zilong and Gao, Ruiqi and Wang, Wenguan and Zhu Song-Chun and Wu, Ying Nian},
    booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2021}
}