3D-R2N2-PyTorch

This is a pytorch version of 3D-R2N2. Original repo: https://github.com/chrischoy/3D-R2N2

Installation

The repo was tested with python3.6, cuda 10.1, pytorch 1.4.0. You can follow the instruction below to install the virtual environment.

Get the source code.

git clone https://github.com/heromanba/3D-R2N2-PyTorch.git

Create virtual environment and install required packages.

cd 3D-R2N2-PyTorch
conda create -n 3D-R2N2 python=3.6
conda activate 3D-R2N2
pip install -r requirements.txt

Download pretrained model(ResidualGRUNet), and put checkpoint.pth under output/ResidualGRUNet/default_model.

Google drive link(https://drive.google.com/open?id=1LtNhuUQdAeAyIUiuCavofBpjw26Ag6DP)

Baidu pan link(链接: https://pan.baidu.com/s/12YK4mnQNx9xdCjzV7zx7GA 提取码: 66nf)
Run The predicted object will be saved to prediction.obj.
```
python demo.py
```

Use the same dataset as mentioned in the original repo.

ShapeNet rendered images http://cvgl.stanford.edu/data2/ShapeNetRendering.tgz

ShapeNet voxelized models http://cvgl.stanford.edu/data2/ShapeNetVox32.tgz
Extract data into ShapeNet directory, the file structure in ShapeNet should be like this:
```
ShapeNet/
|
|----ShapeNetRendering/
|
|----ShapeNetVox32/
|
```
Change some parameters. You can change parameters in experiments/scripts/res_gru_net.sh or lib/config.py
Run.
```
bash experiments/scripts/res_gru_net.sh
```

MIT License