PyDnet
Update:
If you are looking Android/iOS implementations of PyDnet, take a look here: https://github.com/FilippoAleotti/mobilePydnet
This repository contains the source code of pydnet, proposed in the paper "Towards real-time unsupervised monocular depth estimation on CPU", IROS 2018. If you use this code in your projects, please cite our paper:
@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}
}
For more details: arXiv
Demo video: youtube
Requirements
Tensorflow 1.8
(recommended)python packages
such as opencv, matplotlib
Run pydnet on webcam stream
To run pydnet, just launch
python webcam.py --checkpoint_dir ./checkpoint/IROS18/pydnet --resolution [1,2,3]
Train pydnet from scratch
Requirements
monodepth (https://github.com/mrharicot/monodepth)
framework by Clément Godard
After you have cloned the monodepth repository, add to it the scripts contained in training_code
folder from this repository (you have to replace the original monodepth_model.py
script).
Then you can train pydnet inside monodepth framework.
Evaluate pydnet on Eigen split
To get results on the Eigen split, just run
python experiments.py --datapath PATH_TO_KITTI --filenames PATH_TO_FILELIST --checkpoint_dir /checkpoint/IROS18/pydnet --resolution [1,2,3]
This script generates disparity.npy
, that can be evaluated using the evaluation tools by Clément Godard