PV-RAFT

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[CVPR 2021] PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds

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PV-RAFT
===

🔥🔥🔥 2023.7 NEWS: The extended version of PV-RAFT has been accepted by **TPAMI 2023**! Checkout `dpvraft` branch to see our newest DPV-RAFT version!

------

This repository contains the PyTorch implementation for paper "PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds" (CVPR 2021)\[[arXiv](https://arxiv.org/abs/2012.00987)\]

## Installation

### Prerequisites
- Python 3.8
- PyTorch 1.8
- torch-scatter
- CUDA 10.2
- RTX 2080 Ti
- tqdm, tensorboard, scipy, imageio, png

```Shell
conda create -n pvraft python=3.8
conda activate pvraft
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
conda install tqdm tensorboard scipy imageio
pip install pypng
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html
```

## Usage

### Data Preparation
We follow [HPLFlowNet](https://web.cs.ucdavis.edu/~yjlee/projects/cvpr2019-HPLFlowNet.pdf) to prepare FlyingThings3D and KITTI datasets. Please refer to [repo](https://github.com/laoreja/HPLFlowNet). Make sure the project structure look like this:
```Shell
PV-RAFT/
    data/
        FlyingThings3D_subset_processed_35m/
        kitti_processed/
    data_preprocess/
    datasets/
    experiments/
    model/
    modules/
    tools/
```
After downloading datasets, we need to preprocess them.
#### FlyingThings3D Dataset
```Shell
python process_flyingthings3d_subset.py --raw_data_path=path_src/FlyingThings3D_subset --save_path=path_dst/FlyingThings3D_subset_processed_35m --only_save_near_pts
```
You should replace `raw_data_path` and `save_path` with your own setting.

#### KITTI Dataset
```Shell
python process_kitti.py --raw_data_path=path_src/kitti --save_path=path_dst/kitti_processed --calib_path=calib_folder_path
```
You should replace `raw_data_path`, `save_path` and `calib_path` with your own setting.

### Train
```Shell
python train.py --exp_path=pv_raft --batch_size=2 --gpus=0,1 --num_epochs=20 --max_points=8192 --iters=8  --root=./
```
where `exp_path` is the experiment folder name and `root` is the project root path. These 20 epochs take about 53 hours on two RTX 2080 Ti.

If you want to train the refine model, please add `--refine` and specify `--weights` parameter as the directory name of the pre-trained model. For example,

```Shell
python train.py --refine --exp_path=pv_raft_refine --batch_size=2 --gpus=0,1 --num_epochs=10 --max_points=8192 --iters=32 --root=./ --weights=./experiments/pv_raft/checkpoints/best_checkpoint.params
```
These 10 epochs take about 38 hours on two RTX 2080 Ti.

### Test
```Shell
python test.py --dataset=KITTI --exp_path=pv_raft --gpus=1 --max_points=8192 --iters=8 --root=./ --weights=./experiments/pv_raft/checkpoints/best_checkpoint.params
```
where `dataset` should be chosen from `FT3D/KITTI`, and `weights` is the absolute path of checkpoint file.

If you want to test the refine model, please add `--refine`. For example,

```Shell
python test.py --refine --dataset=KITTI --exp_path=pv_raft_refine --gpus=1 --max_points=8192 --iters=32 --root=./ --weights=./experiments/pv_raft_refine/checkpoints/best_checkpoint.params
```

### Reproduce results
You can download the checkpoint of stage1 model [here](https://drive.google.com/file/d/1cvOqgInkQOzMWUqyxitV39FNcJ7VJuJW/view?usp=sharing) and refined model [here](https://drive.google.com/file/d/1lFXPIaJ7gep6Usn-1tCKJF6wgPXPsTO3/view?usp=sharing).

## Acknowledgement
Our code is based on [FLOT](https://github.com/valeoai/FLOT). We also refer to [RAFT](https://github.com/princeton-vl/RAFT) and [HPLFlowNet](https://github.com/laoreja/HPLFlowNet).

## Citation 
If you find our work useful in your research, please consider citing:
```
@inproceedings{wei2020pv,
  title={{PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds}},
  author={Wei, Yi and Wang, Ziyi and Rao, Yongming and Lu, Jiwen and Zhou, Jie},
  booktitle={CVPR},
  year={2021}
}
```

PV-RAFT

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