This repository contains the implementation of the model presented in the following paper:
DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptation, Mirco Planamente, Chiara Plizzari, Marco Cannici, Marco Ciccone, Francesco Strada, Andrea Bottino, Matteo Matteucci, Barbara Caputo ArXiv
- Python3
- PyTorch 1.6
Coming soon
Here are described the procedure to reproduce our datasets. Please contact us if you want to have direct access to them.
Real N-Caltech: the Neuromorphic Caltech101 (N-Caltech101) is an event-based conversion of the popular image dataset Caltech101.
Sim N-Caltech: we used the event simulator ESIM to extract the simulated event streams from Caltech101 RGB images. The conversion from still images to video sequences has been done following Video-to-Events.
RGB-E Real ROD: the RGB-D Object Dataset (ROD) is one of the most common benchmarks for object recognition in robotics. We extended it to the event modality by converting the crops provided in RGB-crops to events, following the procedure in Video-to-Events and using ESIM simulator.
RGB-E Syn ROD: a synthetic version of ROD (synROD) has been recently proposed in synROD. We converted the crops provided in syn-RGB-crops to events with the same procedure described above, obtaining a synthetic event version.
Please cite the following paper if you use this code for your researches:
@misc{planamente2021da4event,
title={DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptation},
author={Mirco Planamente and Chiara Plizzari and Marco Cannici and Marco Ciccone and Francesco Strada and Andrea Bottino and Matteo Matteucci and Barbara Caputo},
year={2021},
eprint={2103.12768},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
If you have any question, do not hesitate to contact us:
- Mirco Planamente:
mirco.planamente@polito.it
- Chiara Plizzari:
chiara.plizzari@polito.it
- Marco Cannici:
marco.cannici@polimi.it