Work accepted at NeurIPS'21 [paper, video].

If you use this code in an academic context, please cite our work:

@article{hagenaarsparedesvalles2021ssl,
  title={Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks},
  author={Hagenaars, Jesse and Paredes-Vall\'es, Federico and de Croon, Guido},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

This code allows for the reproduction of the experiments leading to the results in Section 4.1.

This project uses Python >= 3.7.3 and we strongly recommend the use of virtual environments. If you don't have an environment manager yet, we recommend pyenv. It can be installed via:

curl https://pyenv.run | bash

Make sure your ~/.bashrc file contains the following:

export PATH="$HOME/.pyenv/bin:$PATH"
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"

After that, restart your terminal and run:

pyenv update

To set up your environment with pyenv first install the required python distribution and make sure the installation is successful (i.e., no errors nor warnings):

pyenv install -v 3.7.3

Once this is done, set up the environment and install the required libraries:

pyenv virtualenv 3.7.3 event_flow
pyenv activate event_flow

pip install --upgrade pip==20.0.2

cd event_flow/
pip install -r requirements.txt

In this work, we use multiple datasets:

• event_flow/datasets/data/training: UZH-FPV Drone Racing Dataset (Delmerico, ICRA'19)
• event_flow/datasets/data/MVSEC: Multi View Stereo Event Camera Dataset (Zhu, RA-L'18)
• event_flow/datasets/data/ECD: Event-Camera Dataset (Mueggler, IJRR'17)
• event_flow/datasets/data/HQF: High Quality Frames (Stoffregen and Scheerlinck, ECCV'20)

These datasets can be downloaded in the expected HDF5 data format from here, and are expected at event_flow/datasets/data/ (as shown above).

Download size: 19.4 GB. Uncompressed size: 94 GB.

Details about the structure of these files can be found in event_flow/datasets/tools/.

The pretrained models can be downloaded from here, and are expected at event_flow/mlruns/.

In this project we use MLflow to keep track of the experiments. To visualize the models that are available, alongside other useful details and evaluation metrics, run the following from the home directory of the project:

mlflow ui

and access http://127.0.0.1:5000 from your browser of choice.

To estimate optical flow from event sequences from the MVSEC dataset and compute the average endpoint error and percentage of outliers, run:

python eval_flow.py <model_name> --config configs/eval_MVSEC.yml

# for example:
python eval_flow.py LIFFireNet --config configs/eval_MVSEC.yml

where <model_name> is the name of MLflow run to be evaluated. Note that, if a run does not have a name (this would be the case for your own trained models), you can evaluated it through its run ID (also visible through MLflow).

To estimate optical flow from event sequences from the ECD or HQF datasets, run:

python eval_flow.py <model_name> --config configs/eval_ECD.yml
python eval_flow.py <model_name> --config configs/eval_HQF.yml

# for example:
python eval_flow.py LIFFireNet --config configs/eval_ECD.yml

Note that the ECD and HQF datasets lack ground truth optical flow data. Therefore, we evaluate the quality of the estimated event-based optical flow via the self-supervised FWL (Stoffregen and Scheerlinck, ECCV'20) and RSAT (ours, Appendix C) metrics.

Results from these evaluations are stored as MLflow artifacts.

In configs/, you can find the configuration files associated to these scripts and vary the inference settings (e.g., number of input events, activate/deactivate visualization).

Run:

python train_flow.py --config configs/train_ANN.yml
python train_flow.py --config configs/train_SNN.yml

to train an traditional artificial neural network (ANN, default: FireNet) or a spiking neural network (SNN, default: LIF-FireNet), respectively. In configs/, you can find the aforementioned configuration files and vary the training settings (e.g., model, number of input events, activate/deactivate visualization). For other models available, see models/model.py.

Note that we used a batch size of 8 in our experiments. Depending on your computational resources, you may need to lower this number.

During and after the training, information about your run can be visualized through MLflow.

Once you finish using our code, you can uninstall pyenv from your system by:

1. Removing the pyenv configuration lines from your ~/.bashrc.
2. Removing its root directory. This will delete all Python versions that were installed under the $HOME/.pyenv/versions/ directory:

rm -rf $HOME/.pyenv/