ldkong1205/RoboDepth

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RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions

Lingdong Kong^1,2   Shaoyuan Xie³   Hanjiang Hu⁴   Benoit Cottereau⁵   Lai Xing Ng⁶   Wei Tsang Ooi^1
1National University of Singapore    ²CNRS@CREATE    ³Huazhong Univerisity of Science and Technology    ⁴Carnegie Mellon University    ⁵CNRS    ⁶Institute for Infocomm Research, A*STAR

About

RoboDepth is a comprehensive evaluation benchmark designed for probing the robustness of monocular depth estimation algorithms. It includes 18 common corruption types, ranging from weather and lighting conditions, sensor failure and movement, and noises during data processing.

Updates

• [2023.08] - We support robust depth estimation on real-world scenarios, including nuScenes, nuScenes-Night, Cityscapes, and Foggy-Cityscapes. See here for more details.
• [2023.08] - We establish the nuScenes-C benchmark for robust multi-view depth estimation. See here for more details.
• [2023.07] - The technical report of the RoboDepth Challenge is available on arXiv.
• [2023.06] - We have successfully concluded the RoboDepth Challenge! Key statistics of this competition: 226 teams registered at CodaLab, 66 of which made a total number of 1137 valid submissions. More details are included in these slides. We thank the exceptional support from our participants! ❤️
• [2023.06] - We are glad to announce the winning teams of this competition:
  • Track 1: 🥇 OpenSpaceAI, 🥈 USTC-IAT-United, 🥉 YYQ.
  • Track 2: 🥇 USTCxNetEaseFuxi, 🥈 OpenSpaceAI, 🥉 GANCV.
  • Innovation Prize: 🎖️ Scent-Depth, 🎖️ Ensemble, 🎖️ AIIA-RDepth.
• [2023.06] - The video recordings of the RoboDepth Workshop are out. Know more details about how our participants dedicate to improving the robustness of depth estimation models. 🎥
• [2023.05] - Glad to announce that the RoboDepth Challenge will be sponsored by Baidu Research. 🍻
• [2023.01] - The NYUDepth2-C dataset is ready to be downloaded! See here for more details.
• [2023.01] - Evaluation server for Track 2 (fully-supervised depth estimation) is available on this page.
• [2023.01] - Evaluation server for Track 1 (self-supervised depth estimation) is available on this page.
• [2022.11] - We are organizing the RoboDepth Challenge at ICRA 2023. Join the challenge today! 🙋
• [2022.11] - The KITTI-C dataset is ready to be downloaded! See here for more details.

Outline

• Installation
• Data Preparation
• Getting Started
• Model Zoo
• Benchmark
• Idiosyncrasy Analysis
• Create Corruption Sets
• TODO List
• Citation
• License
• Acknowledgements

Installation

Kindly refer to INSTALL.md for the installation details.

Data Preparation

Our datasets are hosted by OpenDataLab.

OpenDataLab is a pioneering open data platform for the large AI model era, making datasets accessible. By using OpenDataLab, researchers can obtain free formatted datasets in various fields.

The RoboDepth Benchmark

Kindly refer to DATA_PREPARE.md for the details to prepare the ¹KITTI, ²KITTI-C, ³NYUDepth2, ⁴NYUDepth2-C, ⁵Cityscapes, ⁶Foggy-Cityscapes, ⁷nuScenes, and ⁸nuScenes-C, datasets.

Competition @ ICRA 2023

Kindly refer to this page for the details to prepare the training and evaluation data associated with the 1st RoboDepth Competition at the 40th IEEE Conference on Robotics and Automation (ICRA 2023).

Getting Started

Kindly refer to GET_STARTED.md to learn more usage about this codebase.

Model Zoo

🚘 - Outdoor Depth Estimation

🏠 - Indoor Depth Estimation

Benchmark

📊 Metrics: The following metrics are consistently used in our benchmark:

• Absolute Relative Difference (the lower the better): $\text{Abs Rel} = \frac{1}{|D|}\sum_{pred\in D}\frac{|gt - pred|}{gt}$ .

• Accuracy (the higher the better): $\delta_t = \frac{1}{|D|}|{\ pred\in D | \max{(\frac{gt}{pred}, \frac{pred}{gt})< 1.25^t}}| \times 100\%$ .

• Depth Estimation Error (the lower the better):

  • $\text{DEE}_1 = \text{Abs Rel} - \delta_1 + 1$ ;
  • $\text{DEE}_2 = \frac{\text{Abs Rel} - \delta_1 + 1}{2}$ ;
  • $\text{DEE}_3 = \frac{\text{Abs Rel}}{\delta_1}$ .

• The second Depth Estimation Error term ($\text{DEE}_2$) is adopted as the main indicator for evaluating model performance in our RoboDepth benchmark. The following two metrics are adopted to compare between models' robustness:

  • mCE (the lower the better): The average corruption error (in percentage) of a candidate model compared to the baseline, which is calculated among all corruption types across five severity levels.
  • mRR (the higher the better): The average resilience rate (in percentage) of a candidate model compared to its "clean" performance, which is calculated among all corruption types across five severity levels.

⚙️ Notation: Symbol ^⭐ denotes the baseline model adopted in mCE calculation.

KITTI-C

NYUDepth2-C

Idiosyncrasy Analysis

For more detailed benchmarking results and to access the pretrained weights used in robustness evaluation, kindly refer to RESULT.md.

Create Corruption Sets

You can manage to create your own "RoboDepth" corrpution sets! Follow the instructions listed in CREATE.md.

TODO List

• Initial release. 🚀
• Add scripts for creating common corruptions.
• Add download link of KITTI-C and NYUDepth2-C.
• Add competition data.
• Add benchmarking results.
• Add evaluation scripts on corruption sets.

Citation

If you find this work helpful, please kindly consider citing our papers:

@article{kong2023robodepth,
  title = {RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions},
  author = {Kong, Lingdong and Xie, Shaoyuan and Hu, Hanjiang and Cottereau, Benoit and Ng, Lai Xing and Ooi, Wei Tsang},
  journal = {arXiv preprint arXiv:23xx.xxxxx}, 
  year = {2023},
}

@article{kong2023robodepth_challenge,
  title = {The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation},
  author = {Kong, Lingdong and Niu, Yaru and Xie, Shaoyuan and Hu, Hanjiang and Ng, Lai Xing and Cottereau, Benoit and Zhao, Ding and Zhang, Liangjun and Wang, Hesheng and Ooi, Wei Tsang and Zhu, Ruijie and Song, Ziyang and Liu, Li and Zhang, Tianzhu and Yu, Jun and Jing, Mohan and Li, Pengwei and Qi, Xiaohua and Jin, Cheng and Chen, Yingfeng and Hou, Jie and Zhang, Jie and Kan, Zhen and Lin, Qiang and Peng, Liang and Li, Minglei and Xu, Di and Yang, Changpeng and Yao, Yuanqi and Wu, Gang and Kuai, Jian and Liu, Xianming and Jiang, Junjun and Huang, Jiamian and Li, Baojun and Chen, Jiale and Zhang, Shuang and Ao, Sun and Li, Zhenyu and Chen, Runze and Luo, Haiyong and Zhao, Fang and Yu, Jingze},
  journal = {arXiv preprint arXiv:2307.15061}, 
  year = {2023},
}

@misc{kong2023robodepth_benchmark,
  title = {The RoboDepth Benchmark for Robust Out-of-Distribution Depth Estimation under Corruptions},
  author = {Kong, Lingdong and Xie, Shaoyuan and Hu, Hanjiang and Cottereau, Benoit and Ng, Lai Xing and Ooi, Wei Tsang},
  howpublished = {\url{https://github.com/ldkong1205/RoboDepth}}, 
  year = {2023},
}

License

This work is under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Sponsor

We thank Baidu Research for the support towards the RoboDepth Challenge.

Acknowledgements

This project is supported by DesCartes, a CNRS@CREATE program on Intelligent Modeling for Decision-Making in Critical Urban Systems.