IID_SfmLearner

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

Image Intrinsic-Based Unsupervised Monocular Depth Estimation in Endoscopy

Bojian Li, Bo Liu, Miao Zhu, Xiaoyan Luo and Fugen Zhou

Overview

📄 Citation

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

@article{Li2024Image,
  author={Li, Bojian and Liu, Bo and Zhu, Miao and Luo, Xiaoyan and Zhou, Fugen},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={Image Intrinsic-Based Unsupervised Monocular Depth Estimation in Endoscopy}, 
  year={2024},
  volume={},
  number={},
  pages={1-11},
  doi={10.1109/JBHI.2024.3400804}}

⚙️ Setup

We ran our experiments with PyTorch 1.11.0, CUDA 11.2, Python 3.8.13 and Ubuntu 18.04.

💾 Datasets

You can download the Endovis or SCARED dataset by signing the challenge rules and emailing them to max.allan@intusurg.com, you can download the Hamlyn dataset from this website.

Endovis split

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

Data structure

The directory of dataset structure is shown as follows:

/path/to/endovis_data/
  dataset1/
    keyframe1/
      left_img/
          000001.png

🖼️ Prediction for a single image

You can download our depth model and predict scaled disparity for a single image or a folder of images with:

python test_simple.py --image_path <your_image_or_folder_path> --model_path <depth_model_path> --output_path <path to save results>

⏳ Training

You can train a model by running the following command:

python train.py --data_path <your_data_path> --log_dir <path_to_save_model>

📊 Evaluation

To prepare the ground truth depth maps run:

python export_gt_depth.py --data_path <your_data_path> --split <your_dataset_type>

You can evaluate a model by running the following command:

python evaluate_depth.py --data_path <your_data_path> --load_weights_folder <your_model_path> --eval_split <your_dataset_type>

✏️Acknowledgement

Our code is based on the implementation of AF-SfMLearner. We thank these authors for their excellent work and repository.