Official Implementation (PyTorch) of "Point Cloud Augmentation with Weighted Local Transformations", ICCV 2021

Related tags

Deep Learning point-cloud iccv2021
Overview

PointWOLF: Point Cloud Augmentation with Weighted Local Transformations

This repository is the implementation of PointWOLF(To appear).

Sihyeon Kim1*, Sanghyeok Lee1*, Dasol Hwang1, Jaewon Lee1, Seong Jae Hwang2, Hyunwoo J. Kim1†, Point Cloud Augmentation with Weighted Local Transformations (ICCV 2021).
1Korea University 2University of Pittsburgh

PointWOLF_main

Installation

Dependencies

  • CUDA 10.2
  • Python 3.7.1
  • torch 1.7.0
  • packages : sklearn, numpy, h5py, glob

Download

Clone repository

$ git clone https://github.com/mlvlab/PointWOLF.git

Download ModelNet40

Notes : When you run the main.py, ModelNet40 is automatically downloaded at .../PointWOLF/data/.
If you want to download dataset on your ${PATH}, see below.

$ cd ${PATH}
$ wget https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip --no-check-certificate
$ unzip modelnet40_ply_hdf5_2048.zip
$ rm modelnet40_ply_hdf5_2048.zip

Runnig the code

train

  • Run the training without PointWOLF & AugTune:
$ python main.py --exp_name=origin --model=dgcnn --num_points=1024 --k=20 --use_sgd=True
  • Run the training with PointWOLF:
$ python main.py --exp_name=PointWOLF --model=dgcnn --num_points=1024 --k=20 --use_sgd=True --PointWOLF
  • Run the training with PointWOLF & AugTune:
$ python main.py --exp_name=PointWOLF_AugTune --model=dgcnn --num_points=1024 --k=20 --use_sgd=True --PointWOLF --AugTune

eval

  • Run the evaluation with trained model located at ${PATH}:
$ python main.py --exp_name=eval --model=dgcnn --num_points=1024 --k=20 --use_sgd=True --eval=True --model_path=${PATH}

Citation

@InProceedings{Kim_2021_ICCV,
    author    = {Kim, Sihyeon and Lee, Sanghyeok and Hwang, Dasol and Lee, Jaewon and Hwang, Seong Jae and Kim, Hyunwoo J.},
    title     = {Point Cloud Augmentation With Weighted Local Transformations},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {548-557}
}

License

MIT License

Acknowledgement

The structure of this codebase is borrowed from DGCNN.

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Comments
  • Reproduced results on ModelNet40

    Reproduced results on ModelNet40

    Reproduction procedure:

    1. run the training command in README, e.g., python main.py --exp_name=origin --model=dgcnn --num_points=1024 --k=20 --use_sgd=True
    2. run the evaluation command in README, e.g., python main.py --exp_name=eval --model=dgcnn --num_points=1024 --k=20 --use_sgd=True --eval=True --model_path=${PATH}

    Reproduced results on ModelNet40 are as follows:

    • origin (DGCNN): test acc: 0.926661, test avg acc: 0.902744
    • PointWOLF: test acc: 0.924230, test avg acc: 0.895413
    • PointWOLF_AugTune: test acc: 0.927877, test avg acc: 0.893279

    Results reported in the paper:

    Screenshot 2022-01-03 at 11 34 16 AM

    The reproduced PointWOLF's results (w/ or w/o AugTune) are slightly lower than the results reported in the original paper. Is that expected? Thanks.

    opened by jiawei-ren 1
  • Visualization

    Visualization

    Hi Authors,

    Thank you so much for releasing the code. Much appreciated.

    Can you please post scripts/code used to visualize the examples (Fig. 1,2,3)?

    Thanks in advance.

    opened by sheshap 1
Owner
MLV Lab (Machine Learning and Vision Lab at Korea University)
MLV Lab (Machine Learning and Vision Lab at Korea University)
GitHub
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