Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence

PyTorch implementaton of the following paper. In this paper, we propose a unified model for unsupervised stereo matching and optical flow estimation using a single neural network.

Paper

Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence
Hsueh-Ying Lai, Yi-Hsuan Tsai, Wei-Chen Chiu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

Please cite our paper if you find it useful for your research. [Project Page]

@inproceedings{lai19cvpr,
 title = {Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence},
 author = {Hsueh-Ying Lai and Yi-Hsuan Tsai and Wei-Chen Chiu},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year = {2019}
}

Example Results

KITTI Dataset

• Our model requires rectified stereo pairs with different timestamps from KITTI for training.
  We use two different splits of KITTI 2015, kitti and eigen, for both training and testing. For additional testing, we test on the validation set of KITTI 2012. You can find them in the filenames folder.
• Download the raw data of KITTI Dataset. This dataset is for training and eigen split evaluation.

wget -i utils/kitti_archives_to_download.txt -P ~/dataset/

• Download KITTI 2015 scene flow dataset and save it in KITTI_PATH. This dataset is for optical flow and kitti split evaluation.

KITTI_PATH/
         |--training/
         |--testing/

Installation

• This code was developed using Python 3.7 & PyTorch 1.0.0 & CUDA 8.0.
• Other requirements: cv2, matplotlib
• Clone this repo

git clone https://github.com/lelimite4444/BridgeDepthFlow
cd BridgeDepthFlow

Training

We use kitti split as example.

python train.py --data_path ~/dataset/
                --filenames_file ./utils/filenames/kitti_train_files_png_4frames.txt
                --checkpoint_path YOUR_CHECKPOINT_PATH

The chosen --type_of_2warp from 0 ~ 2 correponds to three types of different 2warp function in Figure 4 of our paper. The --model_name flag allows you to choose which model you want to train on. We provide the PyTorch version of both monodepth and PWC-Net.

Testing

We use the validation set of KITTI 2015 as example. The ground truth of optical flow includes occluded area.

• You can download our pretrained models from here, the final character of the model name correponds to the type of 2warp in our paper.
• Test on optical flow

python test_flow.py --data_path KITTI_PATH
                    --filenames_file ./utils/filenames/kitti_flow_val_files_occ_200.txt
                    --checkpoint_path YOUR_CHECKPOINT_PATH/TRAINED_MODEL_NAME

• Test on stereo matching

python test_stereo.py --data_path KITTI_PATH
                    --filenames_file ./utils/filenames/kitti_stereo_2015_test_files.txt
                    --checkpoint_path YOUR_CHECKPOINT_PATH/TRAINED_MODEL_NAME

The network will output disparities.npy, containing all the estimated disparities of test data. You need to evaluate it by running:

python utils/evaluate_kitti.py --split kitti --predicted_disp_path ./disparities.npy --gt_path ~/dataset/

Acknowledgement

• The evaluation code of stereo matching and the structure of monodepth is borrowed from monodepth
• The PWC-Net is implemented by NVlabs-PWC-Net
• The warping function Resample2d and custom layers Correlation which PWC-Net relys on are implemented by NVIDIA-flownet2