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GARNet

This repository contains the source code for the paper GARNet: Global-Aware Multi-View 3D Reconstruction Network and the Cost-Performance Tradeoff.

Cite this work

@article{zhu2023garnet,
  title={GARNet: Global-Aware Multi-View 3D Reconstruction Network and the Cost-Performance Tradeoff},
  author={Zhu, Zhenwei and Yang, Liying and Lin, Xuxin and Yang, Lin and Liang, Yanyan},
  journal={Pattern Recognition},
  pages={109674},
  year={2023},
  publisher={Elsevier}
}

Datasets

We use the ShapeNet in our experiments, which are available below:

• ShapeNet rendering images: http://cvgl.stanford.edu/data2/ShapeNetRendering.tgz
• ShapeNet voxelized models: http://cvgl.stanford.edu/data2/ShapeNetVox32.tgz

Pretrained Models

The pretrained models on ShapeNet are available as follows:

• GARNet
• GARNet+
• IMB for GARNet
• IMB for GARNet+

Prerequisites

Clone the Code Repository

git clone https://github.com/GaryZhu1996/GARNet.git

Install Python Denpendencies

cd GARNet
conda env create -f environment.yml

3D Reconstruction Model

For training, please use the following command:

python runner.py

For testing, please follow the steps below:

1. Update the setting of __C.CONST.WEIGHTS in config.py as the path of the reconstruction model;
2. Run the following command:

python runner.py --test --score_only

View-Reduction Approach

For training IMB, please follow the steps below:

1. Update the setting of inference_model_path in core/train_imb.py as the path of the reconstruction model;
2. Run the following command:

python runner_train_imb.py

For testing with view-reduction approach, please follow the steps below:

1. Update the setting of __C.CONST.WEIGHTS in config.py as the path of the reconstruction model;
2. Update the setting of __C.CONST.IMB_WEIGHTS in config.py as the path of the IMB model;
3. Run the following command:

python runner.py --test --score_only

License

This project is open sourced under MIT license.