/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.

Primary LanguagePython

An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering

Nikola Zubić   Pietro Lio  

University of Novi Sad   University of Cambridge

AIAI 2021

Citation

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

Acknowledgment

This idea has been built based on the architecture of Insafutdinov & Dosovitskiy.
Poisson Surface Reconstruction was used for Point Cloud to 3D Mesh transformation.
The GAN architecture (used for texture mapping) is a mixture of Xian's TextureGAN and Li's GAN.