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