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Unsupervised Depth Completion from Visual Inertial Odometry

Project VOICED: Depth Completion from Inertial Odometry and Vision

Tensorflow implementation of Unsupervised Depth Completion from Visual Inertial Odometry

Published in RA-L January 2020 and ICRA 2020

[arxiv] [poster] [talk]

Models have been tested on Ubuntu 16.04 using Python 3.5, 3.6 Tensorflow 1.14, 1.15 on CUDA 10.0

Authors: Alex Wong, Xiaohan Fei, Stephanie Tsuei

If you use this work, please cite our paper:

@article{wong2020unsupervised,
 title={Unsupervised Depth Completion From Visual Inertial Odometry},
  author={Wong, Alex and Fei, Xiaohan and Tsuei, Stephanie and Soatto, Stefano},
  journal={IEEE Robotics and Automation Letters},
  volume={5},
  number={2},
  pages={1899--1906},
  year={2020},
  publisher={IEEE}
}

Table of Contents

  1. Setting up
  2. Training VOICED
  3. Downloading pretrained models
  4. Evaluating VOICED
  5. Related projects
  6. License and disclaimer

For all setup, training and evaluation code below, we assume that your current working directory is in

/path/to/unsupervised-depth-completion-visual-inertial-odometry/tensorflow

to check that this is the case, you can use pwd.

pwd

Setting up your virtual environment

We will create a virtual environment with the necessary dependencies

virtualenv -p /usr/bin/python3.6 voiced-tensorflow-py3env
source voiced-tensorflow-py3env/bin/activate
pip install opencv-python scipy scikit-learn Pillow matplotlib gdown
pip install numpy==1.16.4 gast==0.2.2
pip install tensorflow-gpu==1.14

Setting up your datasets

For datasets, we will use KITTI for outdoors and VOID for indoors

mkdir data
bash bash/setup_dataset_kitti.sh
bash bash/setup_dataset_void.sh

The bash script downloads the VOID dataset using gdown. However, gdown intermittently fails. As a workaround, you may download them via:

https://drive.google.com/open?id=1kZ6ALxCzhQP8Tq1enMyNhjclVNzG8ODA
https://drive.google.com/open?id=1ys5EwYK6i8yvLcln6Av6GwxOhMGb068m
https://drive.google.com/open?id=1bTM5eh9wQ4U8p2ANOGbhZqTvDOddFnlI

which will give you three files void_150.zip, void_500.zip, void_1500.zip.

Assuming you are in the root of the repository, to construct the same dataset structure as the setup script above:

mkdir void_release
unzip -o void_150.zip -d void_release/
unzip -o void_500.zip -d void_release/
unzip -o void_1500.zip -d void_release/
bash bash/setup_dataset_void.sh unpack-only

If you encounter error: invalid zip file with overlapped components (possible zip bomb). Please do the following

export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE

and run the above again.

For more detailed instructions on downloading and using VOID and obtaining the raw rosbags, you may visit the VOID dataset webpage.

In case you already have KITTI and/or VOID downloaded and in the right form, you may also set this up without the bash script

mkdir data
ln -s /path/to/kitti_raw_data data/
ln -s /path/to/kitti_depth_completion data/
ln -s /path/to/void_release data/

python setup/setup_dataset_kitti.py
python setup/setup_dataset_void.py

Training VOICED

To train VOICED on the KITTI dataset, you may run

sh bash/train_voiced_kitti.sh

To train VOICED on the VOID datasets, you may run

sh bash/train_voiced_void.sh

To monitor your training progress, you may use Tensorboard

tensorboard --logdir trained_models/<model_name>

Downloading our pretrained models

To use our KITTI and VOID models, you can download

gdown https://drive.google.com/uc?id=18jr9l1YvxDUzqAa_S-LYTdfi6zN1OEE9
unzip pretrained_models-tensorflow.zip

Note: gdown fails intermittently and complains about permission. If that happens, you may also download the models via:

https://drive.google.com/open?id=18jr9l1YvxDUzqAa_S-LYTdfi6zN1OEE9

We note that the VOID dataset has been improved (size increased from ~40K to ~47K frames) since this work was published in RA-L and ICRA 2020. We thank the individuals who reached out and gave their feedback. Hence, to reflect the changes, we retrained our model on VOID. We achieve slightly better performance than the reported numbers in the paper.

Model MAE RMSE iMAE iRMSE
VGG11 from paper 85.05 169.79 48.92 104.02
VGG11 retrained 82.27 141.99 49.23 99.67

To achieve the results, we trained for 20 epochs and use a starting learning rate of 5 x 10-5 up to the 12th epoch, then 2.5 x 10-5 for 4 epochs, and 1.2 x 10-5 for the remaining 4 epochs. The weight for smoothness (wsm) is changed to 0.15. This is reflected in the train_voiced_void.sh bash script.

Evaluating VOICED

To evaluate the pretrained VOICED on the KITTI dataset, you may run

sh bash/evaluate_voiced_kitti.sh

To evaluate the pretrained VOICED on the VOID dataset, you may run

sh bash/evaluate_voiced_void.sh

You may replace the restore_path and output_path arguments to evaluate your own checkpoints

Related projects

You may also find the following projects useful:

  • MonDi: Monitored Distillation for Positive Congruent Depth Completion (MonDi). A method for blind ensemble distillation that leverages a monitoring validation function to allow student models trained through the distillation process to retain strengths of teachers while minimizing distillation of their weaknesses. This work is published in the European Conference on Computer Vision (ECCV) 2022.
  • KBNet: Unsupervised Depth Completion with Calibrated Backprojection Layers. A fast (15 ms/frame) and accurate unsupervised sparse-to-dense depth completion method that introduces a calibrated backprojection layer that improves generalization across sensor platforms. This work is published as an oral paper in the International Conference on Computer Vision (ICCV) 2021.
  • ScaffNet: Learning Topology from Synthetic Data for Unsupervised Depth Completion. An unsupervised sparse-to-dense depth completion method that first learns a map from sparse geometry to an initial dense topology from synthetic data (where ground truth comes for free) and amends the initial estimation by validating against the image. This work is published in the Robotics and Automation Letters (RA-L) 2021 and the International Conference on Robotics and Automation (ICRA) 2021.
  • AdaFrame: Learning Topology from Synthetic Data for Unsupervised Depth Completion. An adaptive framework for learning unsupervised sparse-to-dense depth completion that balances data fidelity and regularization objectives based on model performance on the data. This work is published in the Robotics and Automation Letters (RA-L) 2021 and the International Conference on Robotics and Automation (ICRA) 2021.
  • VOICED: Unsupervised Depth Completion from Visual Inertial Odometry. An unsupervised sparse-to-dense depth completion method, developed by the authors. The paper introduces Scaffolding for depth completion and a light-weight network to refine it. This work is published in the Robotics and Automation Letters (RA-L) 2020 and the International Conference on Robotics and Automation (ICRA) 2020.
  • VOID: from Unsupervised Depth Completion from Visual Inertial Odometry. A dataset, developed by the authors, containing indoor and outdoor scenes with non-trivial 6 degrees of freedom. The dataset is published along with this work in the Robotics and Automation Letters (RA-L) 2020 and the International Conference on Robotics and Automation (ICRA) 2020.
  • XIVO: The Visual-Inertial Odometry system developed at UCLA Vision Lab. This work is built on top of XIVO. The VOID dataset used by this work also leverages XIVO to obtain sparse points and camera poses.
  • GeoSup: Geo-Supervised Visual Depth Prediction. A single image depth prediction method developed by the authors, published in the Robotics and Automation Letters (RA-L) 2019 and the International Conference on Robotics and Automation (ICRA) 2019. This work was awarded Best Paper in Robot Vision at ICRA 2019.
  • AdaReg: Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction. A single image depth prediction method that introduces adaptive regularization. This work was published in the proceedings of Conference on Computer Vision and Pattern Recognition (CVPR) 2019.

We also have works in adversarial attacks on depth estimation methods and medical image segmentation:

  • SUPs: Stereoscopic Universal Perturbations across Different Architectures and Datasets.. Universal advesarial perturbations and robust architectures for stereo depth estimation, published in the Proceedings of Computer Vision and Pattern Recognition (CVPR) 2022.
  • Stereopagnosia: Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations. Adversarial perturbations for stereo depth estimation, published in the Proceedings of AAAI Conference on Artificial Intelligence (AAAI) 2021.
  • Targeted Attacks for Monodepth: Targeted Adversarial Perturbations for Monocular Depth Prediction. Targeted adversarial perturbations attacks for monocular depth estimation, published in the proceedings of Neural Information Processing Systems (NeurIPS) 2020.
  • SPiN : Small Lesion Segmentation in Brain MRIs with Subpixel Embedding. Subpixel architecture for segmenting ischemic stroke brain lesions in MRI images, published in the Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Brain Lesion Workshop 2021 as an oral paper.

License and disclaimer

This software is property of the UC Regents, and is provided free of charge for research purposes only. It comes with no warranties, expressed or implied, according to these terms and conditions. For commercial use, please contact UCLA TDG.