# Point-Scatter

## 1. Requirements
PyTorch >= 1.7.0 < 1.11.0;
python >= 3.7;
CUDA >= 9.0;
GCC >= 4.9;
torchvision;

```
pip install -r requirements.txt
```

```
# Chamfer Distance & emd
cd ./extensions/chamfer_dist
python setup.py install --user
cd ./extensions/emd
python setup.py install --user
# Pytorch3d
git clone https://github.com/facebookresearch/pytorch3d.git
cd pytorch3d
export CUB_HOME=/usr/local/cuda/include/
FORCE_CUDA=1 python setup.py install
```

## 2. Datasets

We use homemake dataset.

## 3. Point-MAE Pre-training
To pretrain Point-In-Context on our dataset, run the following command. If you want to try different models or masking ratios etc., change the mask_ration in /cfg/pretrain.yaml

```
CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/PIC_Sep.yaml --exp_name <output_file_name>
```
