ER-Depth: Enhancing the Robustness of Self-Supervised Monocular Depth Estimation in Challenging Scenes

Ziyang Song*, Ruijie Zhu*, Chuxin Wang, Jiacheng Deng, Jianfeng He,
Tianzhu Zhang,
*Equal Contribution.
University of Science and Technology of China
TOMM 2025

                       

The two-stage training framework of ER-Depth. In the first stage, we train DepthNet and PoseNet with the perturbation-invariant depth consistency loss. In the second stage, we leverage the teacher network to generate pseudo labels and construct a distillation loss to train the student network. Notably, we propose a depth consistency-based filter (DC-Filter) and a geometric consistency-based filter (GC-Filter) to filter out unreliable pseudo labels.

News

• 16 Dec. 2023: The code is now available.
• 28 Nov. 2023: The project website was released.
• 12 Oct. 2023: ER-Depth released on arXiv.

Installation

Please refer to dataset_prepare.md for dataset preparation and get_started.md for installation.

Running

We provide example bash commands to run training or testing. Please modify these files according to your own configuration before running.

Training

First stage training:

bash train_first_stage.sh train first_stage_model 2 4 

Second stage training:

bash train_second_stage.sh train second_stage_model 2 4 

Testing

Evaluate the model on KITTI dataset：

bash evaluate_kitti.sh

Evaluate the model on KITTI-C dataset：

bash evaluate_kittic.sh

Results

We provide the official weights of ER-Depth (the first stage model) and ER-Depth* (the second stage model) on Google Drive. Their experimental results on KITTI and KITTI-C are as below.

KITTI

Methods	AbsRel	SqRel	RMSE	RMSE log	a1	a2	a3
ER-Depth	0.100	0.708	4.367	0.175	0.896	0.966	0.984
ER-Depth*	0.100	0.689	4.315	0.173	0.896	0.967	0.985

KITTI-C

Methods	AbsRel	SqRel	RMSE	RMSE log	a1	a2	a3
ER-Depth	0.115	0.841	4.749	0.189	0.869	0.958	0.982
ER-Depth*	0.111	0.807	4.651	0.185	0.874	0.960	0.983

Bibtex

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

@article{song2025er,
  title={Er-depth: Enhancing the robustness of self-supervised monocular depth estimation in challenging scenes},
  author={Song, Ziyang and Zhu, Ruijie and Wang, Jing and Wang, Chuxin and He, Jianfeng and Deng, Jiacheng and Yang, Wenfei and Zhang, Tianzhu},
  journal={ACM Transactions on Multimedia Computing, Communications and Applications},
  volume={21},
  number={12},
  pages={1--23},
  year={2025},
  publisher={ACM New York, NY}
}

Acknowledgements

The code is based on MonoDepth2, MonoViT, and RoboDepth.