• PaddleMono: A Unified Framework for Monocular Depth Estimation
• • Implemented Algorithms
  • Installation
  • Dataset
  • Usage
  • Customization
  • Results
  • • KITTI
  • Contribution
  • Acknowledgement
  • References
  • Contact

PaddleMono: A Unified Framework for Monocular Depth Estimation

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A lightweight, easy-to-extend, easy-to-learn, high-performance, and for-fair-comparison toolkit based on PaddlePaddle for Monocular Depth Estimation. It is a part of the Paddledepth project.

Implemented Algorithms

As initial version, we support the following algoirthms. We are working on more algorithms. Of course, you are welcome to add your algorithms here.

1. Monodepth2 (ICCV2019)[1]
2. MLDA-Net (TIP2021)[2]
3. Depth Hints (ICCV2019)[3]

Please click the hyperlink of each algorithm for more detailed explanation.

Installation

You can either git clone this whole repo by:

git clone https://github.com/PaddlePaddle/PaddleDepth.git
cd Paddle-Mono
pip install -r requirements.txt

Please use PaddleMono in python 3.9.

Dataset

see guidance in dataset_prepare for dataset preparation.

Usage

1. Modify the configuration file in the corresponding directories in configs.
2. Run the train.py with specified config, for example, python train.py --config configs/monodepthv2/mdp.yml

• We provide shell scripts to help you reproduce our experimental results: bash configs/monodepth/mdp.sh.

Customization

It is easy to design your own method with following steps:

1. Check and write your own model's to model
2. Write your own config file (.sh or .yml)

Results

We present results of our implementations on the popular KITTI benchmarks with Eigen split.

Note: We did not optimize the model results by additional tricks, so you can directly use the provided shell scripts to reproduce the accuracy we report in the table.

KITTI

Method	abs_rel	sq_rel	rms	log_rms	a1	a2	a3
Monodepth2_640x192	0.112	0.839	4.846	0.193	0.875	0.957	0.980
Monodepth2_1024x32	0.112	0.833	4.748	0.191	0.880	0.960	0.981
Depth Hints_640x192	0.110	0.818	4.728	0.189	0.881	0.959	0.981
Depth Hints_1024x320	0.109	0.794	4.474	0.185	0.887	0.963	0.982
MLDANet_640x192	0.108	0.829	4.678	0.184	0.885	0.962	0.983

Contribution

The toolkit is under active development and contributions are welcome!
Feel free to submit issues or emails to ask questions or contribute your code.
If you would like to implement new features, please submit a issue or emails to discuss with us first.

Acknowledgement

PaddleDepth is an open source project that is contributed by researchers and engineers
from various colleges and companies.
We appreciate all the contributors who implement their methods or add new features,
as well as users who give valuable feedbacks.
We wish that the toolbox and benchmark could serve the growing research community by
providing a flexible toolkit to reimplement existing methods and develop their new algorithms.

References

[1] Godard C, Mac Aodha O, Firman M, et al. Digging into self-supervised monocular depth estimation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 3828-3838.

[2] Song X, Li W, Zhou D, et al. MLDA-Net: Multi-level dual attention-based network for self-supervised monocular depth estimation[J]. IEEE Transactions on Image Processing, 2021, 30: 4691-4705.

[3] Watson J, Firman M, Brostow G J, et al. Self-supervised monocular depth hints[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 2162-2171.

Contact

• Yian Zhao: zhaoyian.zh@gmail.com
• Zhelun Shen: shenzhelun@pku.edu.cn