CCMR

[WACV24] This is the inference code of our CCMR optical flow estimation method.

CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning

WACV 2024
Azin Jahedi, Maximilian Luz, Marc Rivinius and Andrés Bruhn

If you find our work useful please cite via BibTeX.

Requirements

The code has been tested with PyTorch 1.10.2+cu113. Install the required dependencies via

pip install -r requirements.txt

Alternatively you can also manually install the following packages in your virtual environment:

• torch, torchvision, and torchaudio (e.g., with --extra-index-url https://download.pytorch.org/whl/cu113 for CUDA 11.3)
• matplotlib
• scipy
• tensorboard
• opencv-python
• tqdm
• parse
• timm
• flowpy

Pre-Trained Checkpoints

You can find our trained models in the release assets.

Datasets

Datasets are expected to be located under ./data in the following layout:

./data
  ├── kitti15                   # KITTI 2015
  │  └── dataset
  │     ├── testing/...
  │     └── training/...
  └── sintel                    # Sintel
     ├── test/...
     └── training/...

Running CCMR(+)

For running CCMR+ on MPI Sintel images you need about 4.5 GB of GPU VRAM. CCMR (the 3-scale version) needs about 3 GBs of VRAM, using the following Cuda module.

To compile the CUDA correlation module run the following once:

cd alt_cuda_corr && python setup.py install && cd ..

You can download the pre-trained models from the release page. To reproduce our benchmark results after finetuning run:

python evaluate.py --model_type "CCMR+" --model models/CCMR+_sintel.pth --dataset sintel_test
python evaluate.py --model_type "CCMR+" --model models/CCMR+_kitti.pth --dataset kitti_test

python evaluate.py --model_type "CCMR" --model models/CCMR_sintel.pth --dataset sintel_test
python evaluate.py --model_type "CCMR" --model models/CCMR_kitti.pth --dataset kitti_test

License

• Our code is licensed under the BSD 3-Clause No Military License. See LICENSE.
• The provided checkpoints are under the CC BY-NC-SA 3.0 license.

Acknowledgement

Parts of this repository are adapted from RAFT (license), MS-RAFT+ (license), and XCiT (license). We thank the authors.