2dimageto3dmodel

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

pretrained_weights_p3d

Citation

Besides AIAI 2021, our paper is in a Springer's book entitled "Artificial Intelligence Applications and Innovations": link



Please, cite our paper if you find this code useful for your research.

@article{zubic2021effective,
  title={An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering},
  author={Zubi{\'c}, Nikola and Li{\`o}, Pietro},
  journal={arXiv preprint arXiv:2103.03390},
  year={2021}
}

Prerequisites

  • Download code:

    Git clone the code with the following command:

    git clone https://github.com/NikolaZubic/2dimageto3dmodel.git
    
  • Open the project with Conda Environment (Python 3.7)

  • Install packages:

    conda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch
    

    Then git clone Kaolin library in the root (2dimageto3dmodel) folder with the following commit and run the following commands:

    cd kaolin
    python setup.py install
    pip install --no-dependencies nuscenes-devkit opencv-python-headless scikit-learn joblib pyquaternion cachetools
    pip install packaging
    

Run the program

Run the following commands from the root/code/ (2dimageto3dmodel/code/) directory:

python main.py --dataset cub --batch_size 16 --weights pretrained_weights_cub --save_results

for the CUB Birds Dataset.


python main.py --dataset p3d --batch_size 16 --weights pretrained_weights_p3d --save_results

for the Pascal 3D+ Dataset.

The results will be saved at 2dimageto3dmodel/code/results/ path.

Continue training

To continue the training process:

Run the following commands (without --save_results) from the root/code/ (2dimageto3dmodel/code/) directory:

python main.py --dataset cub --batch_size 16 --weights pretrained_weights_cub

for the CUB Birds Dataset.


python main.py --dataset p3d --batch_size 16 --weights pretrained_weights_p3d

for the Pascal 3D+ Dataset.

License

MIT

GitHub

https://github.com/NikolaZubic/2dimageto3dmodel