tf-monodepth2

This is tensorflow(unofficial) implementation for the method in

Digging into Self-Supervised Monocular Depth Prediction

Clément Godard, Oisin Mac Aodha, Michael Firman and Gabriel J. Brostow

arXiv 2018

Code mainly based on [SFMLearner](https://github.com/tinghuiz/SfMLearner) and [SuperPoint](https://github.com/rpautrat/SuperPoint)

Update: fix testing bug for input data normalization, thanks @JiatianWu

Current pretrained model result is slightly low than paper.

If you find this work useful in your research please consider citing author's paper:

@article{monodepth2,
  title     = {Digging into Self-Supervised Monocular Depth Prediction},
  author    = {Cl{\'{e}}ment Godard and
               Oisin {Mac Aodha} and
               Michael Firman and
               Gabriel J. Brostow},
  journal = {arXiv:1806.01260},
  year = {2018}
}

Eval Result (with pretrained model link)

model_name	abs_rel	sq_rel	rms	log_rms	δ<1.25	δ<1.25^2	δ<1.25^3
mono_640x192_nopt(paper)	0.132	1.044	5.142	0.210	0.845	0.948	0.977
mono_640x192_nopt(ours)	0.139	1.1293	5.4190	0.2200	0.8299	0.9419	0.9744
mono_640x192_pt(paper)	0.115	0.903	4.863	0.193	0.877	0.959	0.982
mono_640x192_pt(ours)	0.120	0.8702	4.888	0.194	0.861	0.957	0.982

Demonstration (Click Image For Youtube Video)

Prerequisites

This codebase was developed and tested with Tensorflow 1.6.0, CUDA 8.0 and Ubuntu 16.04.

Setup

Assuming a fresh Anaconda distribution, you can install the dependencies with:

conda env create -f environment.yml
conda activate tf-monodepth2

Preparing training data

In order to train the model using the provided code, the data needs to be formatted in a certain manner.

For KITTI, first download the dataset using this script provided on the official website, and then run the following command

python data/prepare_train_data.py --dataset_dir=/path/to/raw/kitti/dataset/ --dataset_name='kitti_raw_eigen' --dump_root=/path/to/resulting/formatted/data/ --seq_length=3 --img_width=416 --img_height=128 --num_threads=4

Training(Only Monocular)

First of all, set dataset/saved_log path at monodepth2_kitti.yml

python monodepth2.py train config/monodepth2_kitti.yml your_saved_model_name

Testing

python monodepth2.py test config/monodepth2_kitti.yml your_pretrained_model_name

Get pretrained model download link from Eval Result chart

Evaluation

• First we need to save predicted depth image into npy file

python monodepth2.py eval config/monodepth2_kitti_eval.yml your_pretrained_model_name depth

Save destination should be setted in monodepth2_kitti_eval.yml.

• Then we use evaluation code to compute error result:

cd kitti_eval
python2 eval_depth.py --kitti_dir=/your/kitti_data/path/ --pred_file=/your/save/depth/npy/path/ --test_file_list=../data/kitti/test_files_eigen.txt

Note: please use python2 to execute this bash.

kitti_eval code from Zhou's SFMLearner

Pose evaluation code to be completed.

Reference Codes

• Monodepth2

  • https://github.com/nianticlabs/monodepth2

• SfmLearner

  • https://github.com/tinghuiz/SfMLearner

• SuperPoint

  • https://github.com/rpautrat/SuperPoint

• resnet-18-tensorflow

  • https://github.com/dalgu90/resnet-18-tensorflow

TODO

• Auto-Mask loss described in paper
• ResNet-18 Pretrained Model code
• Testing part
• Evaluation for pose estimation
• stereo and mono+stereo training