This repository contains the source code to run PyDNet on mobile devices.
In v2.0, we changed the procedure and the data used for training. More information will be provided soon...
Moreover, we build also a web-based demonstration of the same network! You can try it now here. The model runs directly on your browser, so anything to install!
The iOS demo has been developed by Giulio Zaccaroni.
XCode is required to build the app, moreover you need to sign in with your AppleID and trust yourself as certified developer.
The code will be released soon
Code is licensed under APACHE version 2.0 license. Weights of the network can be used for research purposes only.
If you use this code in your projects, please cite our paper:
@article{aleotti2020real,
title={Real-time single image depth perception in the wild with handheld devices},
author={Aleotti, Filippo and Zaccaroni, Giulio and Bartolomei, Luca and Poggi, Matteo and Tosi, Fabio and Mattoccia, Stefano},
journal={Sensors},
volume={21},
year={2021}
}
@inproceedings{pydnet18,
title = {Towards real-time unsupervised monocular depth estimation on CPU},
author = {Poggi, Matteo and
Aleotti, Filippo and
Tosi, Fabio and
Mattoccia, Stefano},
booktitle = {IEEE/JRS Conference on Intelligent Robots and Systems (IROS)},
year = {2018}
}
More info about the work can be found at these links:
- Real-time single image depth perception in the wild with handheld devices, Arxiv
- PyDNet paper
- PyDNet code
For questions, please send an email to filippo.aleotti2@unibo.it