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Video to Events: Recycling Video Datasets for Event Cameras

This repository contains code that implements video to events conversion as described in Gehrig et al. CVPR'20. The paper can be found here

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

Daniel Gehrig, Mathias Gehrig, Javier Hidalgo-Carrió, Davide Scaramuzza, "Video to Events: Recycling Video Datasets for Event Cameras", The Conference on Computer Vision and Pattern Recognition (CVPR), 2020

@InProceedings{Gehrig_2020_CVPR,
  author = {Daniel Gehrig and Mathias Gehrig and Javier Hidalgo-Carri\'o and Davide Scaramuzza},
  title = {Video to Events: Recycling Video Datasets for Event Cameras},
  booktitle = {{IEEE} Conf. Comput. Vis. Pattern Recog. (CVPR)},
  month = {June},
  year = {2020}
}

Installation

Clone the repo recursively with submodules

git clone git@github.com:uzh-rpg/rpg_vid2e.git --recursive

Installation with Anaconda

Adapt the CUDA toolkit version according to your setup.

cuda_version=10.1

conda create -y -n vid2e python=3.7
conda activate vid2e
conda install -y pytorch torchvision cudatoolkit=$cuda_version -c pytorch
conda install -y -c conda-forge opencv tqdm scikit-video eigen boost boost-cpp pybind11

Build the python bindings for ESIM

cd esim_py
pip install .

Adaptive Upsampling

This package provides code for adaptive upsampling with frame interpolation based on Super-SloMo

Consult the README for detailed instructions and examples.

esim_py

This package exposes python bindings for ESIM which can be used within a training loop.

For detailed instructions and example consult the README