PyTorch Lightning Optical Flow
This is a collection of state-of-the-art deep model for estimating optical flow. The main goal is to provide a unified framework where multiple models can be trained and tested more easily.
The work and code from many others are present here. I tried to make sure everything is properly referenced, but please let me know if I missed something.
This is still under development, so some things may not work as intended. I plan to add more models in the future, as well keep improving the platform.
- DICL-Flow https://arxiv.org/abs/2010.14851
- FlowNet - https://arxiv.org/abs/1504.06852
- FlowNet2 - https://arxiv.org/abs/1612.01925
- HD3 - https://arxiv.org/abs/1812.06264
- IRR - https://arxiv.org/abs/1904.05290
- LCV - https://arxiv.org/abs/2007.11431
- LiteFlowNet https://arxiv.org/abs/1805.07036
- LiteFlowNet2 https://arxiv.org/abs/1903.07414
- LiteFlowNet3 https://arxiv.org/abs/2007.09319
- MaskFlownet https://arxiv.org/abs/2003.10955
- PWCNet - https://arxiv.org/abs/1709.02371
- RAFT - https://arxiv.org/abs/2003.12039
- ScopeFlow - https://arxiv.org/abs/2002.10770
- STaRFlow - https://arxiv.org/abs/2007.05481
- VCN - https://papers.nips.cc/paper/2019/file/bbf94b34eb32268ada57a3be5062fe7d-Paper.pdf
Read more details about the models on https://ptlflow.readthedocs.io/en/latest/models/models_list.html.
Disclaimer: These results are the ones obtained by evaluating the available models in this framework in my machine. Your results may be different due to differences in hardware and software. I also do not guarantee that the results of each model will be similar to the ones presented in the respective papers or other original sources. If you need to replicate the original results from a paper, you should use the original implementations.
Please take a look at the documentation to learn how to install and use PTLFlow.
You can also check the notebooks below running on Google Colab for some practical examples: