The official implementation of CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing

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Deep Learning CSGStumpNet
Overview

CSGStumpNet

The official implementation of CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing

Paper | Project page

Citation

If you find our work interesting and benifits your research, please consider citing:

@article{ren2021csgstump,
  title={CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing},
  author={Daxuan Ren, Jianmin Zheng, Jianfei Cai, Jiatong Li, Haiyong Jiang, Zhongang Cai, Junzhe Zhang, Liang Pan, Mingyuan Zhang, Haiyu Zhao, Shuai Yi},
  journal={ICCV},
  year={2021}
}

Setup

Install envoriment:

We recommand using Anaconda to set the envoriment, once Anacodna in installed, run the following command.

conda create --name CSGStumpNet python=3.7
conda activate CSGStumpNet
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch
conda install -c open3d-admin open3d=0.9
conda install numpy
conda install pymcubes
conda install tensorboard
conda install scipy
pip install tqdm

Datasets and pre-trained weights

Dataset

We use the pre-prepared dataset from OccNet(consider citing them), you can download the data by

mkdir data
cd data
wget https://s3.eu-central-1.amazonaws.com/avg-projects/occupancy_networks/data/dataset_small_v1.1.zip
unzip dataset_small_v1.1.zip

If you want to prepare data yourself (maybe you want to generate the watertight mesh etc.), please refer to this link.

Pre-Train Weights

The original trained weights are no longer compatiable with the the restructred code (this repo) :( re-training is in progress (This may take some time).

Evaluate using pre-trian weights

python eval.py --config_path ./configs/plane_256_256.json

Train from stratch

python train.py --config_path ./configs/plane_256_256.json

Evaluation

python metrics.py --config_path ./configs/plane_256_256.json

License

This project is licensed under the terms of the MIT license (see LICENSE for details).

Comments
  • Release code of complement layer

    Release code of complement layer

    Hi, I am interested in your work.

    However, I find the first complement layer code is missing in your code model.py.

    Will you plan to release the code of that part?

    Looking forward to your reply, thank you!

    Best.

    opened by FENGGENYU 5
  • Why do you use infinitely long cylinders and cones?

    Why do you use infinitely long cylinders and cones?

    Hi, thanks for your excellent work! But I'm confused that why you use infinitely long cylinders and cones when computing the sdf. Why there is no "height" parameter associated with these two types of primitives? I think they need to be bounded to form the final shape. Could you please explain this for me? Thank you in advance!

    opened by bluestyle97 4
  • Release pretrained weights

    Release pretrained weights

    Hi,

    This is an interesting work and thanks for sharing your code!

    I just want to know when will you share the pre-trained weights? I'd like to try it in my project!

    Thank you!

    opened by Christine0924 4
  • Question about complement layer

    Question about complement layer

    Hi, daxuan I noticed that in model.py, the complement layer was not written after connection decoder. Is it a trick to make the end-to-end method perform better under ShapeNet dataset or just an ignorance? I'm asking this for the reason that my own dataset for mechanical parts have many through-hole structures, which is not topologically isomorphic to those in ShapeNet, and I'm trying to figure out why the training process always ignored the hole structure. Till now, I've excluded the reason of sampling strategy and loss function. Do you think it will help if the complement layer is added?

    opened by Farewell-ME 3
  • question about avg_test_loss_recon

    question about avg_test_loss_recon

    Hi daxuan, Why is the value avg_test_loss_recon divided twice by test_iter when printed to console as "loss_recon"? It occurs both in train.py and eval.py. Best regards

    opened by Farewell-ME 2
  • Sharing models with the Hugging Face Hub

    Sharing models with the Hugging Face Hub

    Hi there!

    CSGStumpNet seems interesting! I see you currently save your model checkpoint in this repo. Would you be interested in sharing the model in the Hugging Face Hub?

    The Hub offers free hosting of over 20K models, and it would make your work more accessible and visible to the rest of the ML community. Some of the benefits of sharing your models would be:

    • versioning
    • commit history and diffs
    • repos provide useful metadata about their tasks, languages, metrics, etc
    • we could add a widget for users to try the model directly in the browser

    Creating the repos and adding new models should be a relatively straightforward process if you've used Git before. This is a step-by-step guide explaining the process in case you're interested. Please let us know if you would be interested and if you have any questions.

    Happy to hear your thoughts, Omar and the Hugging Face team

    opened by osanseviero 1
  • Question about Primitive Loss

    Question about Primitive Loss

    Hi, thank you for this interesting work and congrats on publishing at ICCV.

    I have a question about the implementation of the primitive loss term. In the paper, it is

    But in the implementation it is: https://github.com/kimren227/CSGStumpNet/blob/e23c6fbd20097000b886221abd70def34ac2a15b/loss.py#L11

    Which seems like it is this expression instead:

    I suppose this is equivalent to the first equation when all testing points are outside the primitive, but once there are testing points inside this loss term seems to pull the surface so that no testing points are too far inside (since it pushes the most negative value toward 0). Is this intentional?

    Might the correct implementation be as follows?

    primitive_loss = torch.mean((primitive_sdf**2).min(dim=1)[0]) * self.scale
    opened by haoala 10
Owner
Daxuan
PhD Student at Nanyang Technological University
Daxuan
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