pc4consistentdepth

This is the reference PyTorch implementation for training and testing temporally consistent depth estimation models using the method described in

Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion

Zeeshan Khan Suri

⚙️ Requirements

Run setup.sh, which does the following steps respectively

1. Clone monodepth2.
2. Copy this file in the monodepth2 directory.
3. Edit train.py from from trainer import Trainer to from pc4consistentdepth_trainer import Trainer.

Then, you're ready to go.

1. By default, uses cyclic consistency with 0.1 weight. You can change options by adding use_pose_consistency_loss and pose_consistency_loss_weight options or by editing pc4consistentdepth_trainer.py's __init__ function.
2. Follow instructions from monodepth2.

👩‍⚖️ License

The pc4consistentdepth_trainer.py code is released under MIT License, Copyright © Zeeshan Khan Suri, Denso ADAS Engineering Services GmbH, 2023.

so3_utils.py is taken from Pytorch3D, with it's respective BSD-style license.

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If you find our work useful in your research please consider citing our paper:

@InProceedings{10.1007/978-3-031-31438-4_23,
author="Suri, Zeeshan Khan",
editor="Gade, Rikke and Felsberg, Michael and K{\"a}m{\"a}r{\"a}inen, Joni-Kristian",
title="Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-Motion",
booktitle="Image Analysis",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="340--353",
isbn="978-3-031-31438-4",
doi={10.1007/978-3-031-31438-4_23}
}