ShuweiShao / AF-SfMLearner

[MedIA2022 & ICRA2021] Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue

AF-SfMLearner

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

Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue

Shuwei Shao, Zhongcai Pei, Weihai Chen, Wentao Zhu, Xingming Wu, Dianmin Sun and Baochang Zhang

accepted by Medical Image Analysis (arXiv pdf)

and

Self-Supervised Learning for Monocular Depth Estimation on Minimally Invasive Surgery Scenes

Shuwei Shao, Zhongcai Pei, Weihai Chen, Baochang Zhang, Xingming Wu, Dianmin Sun and David Doermann

ICRA 2021 (pdf).

Overview

✏️ 📄 Citation

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

@article{shao2022self,
  title={Self-Supervised monocular depth and ego-Motion estimation in endoscopy: Appearance flow to the rescue},
  author={Shao, Shuwei and Pei, Zhongcai and Chen, Weihai and Zhu, Wentao and Wu, Xingming and Sun, Dianmin and Zhang, Baochang},
  journal={Medical image analysis},
  volume={77},
  pages={102338},
  year={2022},
  publisher={Elsevier}
}

@inproceedings{shao2021self,
  title={Self-Supervised Learning for Monocular Depth Estimation on Minimally Invasive Surgery Scenes},
  author={Shao, Shuwei and Pei, Zhongcai and Chen, Weihai and Zhang, Baochang and Wu, Xingming and Sun, Dianmin and Doermann, David},
  booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={7159--7165},
  year={2021},
  organization={IEEE}
}

⚙️ Setup

We ran our experiments with PyTorch 1.2.0, torchvision 0.4.0, CUDA 10.2, Python 3.7.3 and Ubuntu 18.04.

🖼️ Prediction for a single image or a folder of images

You can predict scaled disparity for a single image or a folder of images with:

CUDA_VISIBLE_DEVICES=0 python test_simple.py --model_path <your_model_path> --image_path <your_image_or_folder_path>

💾 Datasets

You can download the Endovis or SCARED dataset by signing the challenge rules and emailing them to [e-mail], the EndoSLAM dataset, the SERV-CT dataset, and the Hamlyn dataset.

Endovis split

The train/test/validation split for Endovis dataset used in our works is defined in the splits/endovis folder.

Endovis data preprocessing

We use the ffmpeg to convert the RGB.mp4 into images.png:

find . -name "*.mp4" -print0 | xargs -0 -I {} sh -c 'output_dir=$(dirname "$1"); ffmpeg -i "$1" "$output_dir/%10d.png"' _ {}

We only use the left frames in our experiments and please refer to extract_left_frames.py. For dataset 8 and 9, we rephrase keyframes 0-4 as keyframes 1-5.

Data structure

The directory of dataset structure is shown as follows:

/path/to/endovis_data/
  dataset1/
    keyframe1/
      image_02/
        data/
          0000000001.png

⏳ Endovis training

Stage-wise fashion:

Stage one:

CUDA_VISIBLE_DEVICES=0 python train_stage_one.py --data_path <your_data_path> --log_dir <path_to_save_model (optical flow)>

Stage two:

CUDA_VISIBLE_DEVICES=0 python train_stage_two.py --data_path <your_data_path> --log_dir <path_to_save_model (depth, pose, appearance flow, optical flow)> --load_weights_folder <path_to_the_trained_optical_flow_model_in_stage_one>

End-to-end fashion:

CUDA_VISIBLE_DEVICES=0 python train_end_to_end.py --data_path <your_data_path> --log_dir <path_to_save_model (depth, pose, appearance flow, optical flow)>

📊 Endovis evaluation

To prepare the ground truth depth maps run:

CUDA_VISIBLE_DEVICES=0 python export_gt_depth.py --data_path endovis_data --split endovis

...assuming that you have placed the endovis dataset in the default location of ./endovis_data/.

The following example command evaluates the epoch 19 weights of a model named mono_model:

CUDA_VISIBLE_DEVICES=0 python evaluate_depth.py --data_path <your_data_path> --load_weights_folder ~/mono_model/mdp/models/weights_19 --eval_mono

Appearance Flow

Depth Estimation

Visual Odometry

3D Reconstruction

📦 Model zoo

Model	Abs Rel	Sq Rel	RMSE	RMSE log	Link
Stage-wise (ID 5 in Table 8)	0.059	0.435	4.925	0.082	baidu (code:n6lh); google
End-to-end (ID 3 in Table 8)	0.059	0.470	5.062	0.083	baidu (code:z4mo); google
ICRA	0.063	0.489	5.185	0.086	baidu (code:wbm8); google

Important Note

If you use the latest PyTorch version,

Note1: please try to add 'align_corners=True' to 'F.interpolate' and 'F.grid_sample' when you train the network, to get a good camera trajectory.

Note2: please revise color_aug=transforms.ColorJitter.get_params(self.brightness,self.contrast,self.saturation,self.hue) to color_avg=transforms.ColorJitter(self.brightness,self.contrast,self.saturation,self.hue).

Contact

If you have any questions, please feel free to contact [e-mail].

Acknowledgement

Our code is based on the implementation of Monodepth2. We thank Monodepth2's authors for their excellent work and repository.