Offical Code Implementation for MIOL in 3D laparoscopy

Real-Time and High-Accuracy Switchable Stereo Depth Estimation Method Utilizing Self-Supervised Online Learning Mechanism for MIS.
Jieyu Zheng, Xiaojian Li*, Xin Wang*, Haojun Wu, Ling Li, Xiang Ma, Shanlin Yang

💾 Data Description

To pretrain the network SSDNet, you will need to download the required datasets.

• Sceneflow (DispNet/FlowNet2.0 dataset subsets are enough)

The structure of the downloaded dataset is as follows

├── SceneFlowSubData
    ├── disparity_occlusions
        ├── train
        ├── val
    ├── frames_cleanpass
        ├── train
        ├── val
    ├── frames_disparity
        ├── train
        ├── val

To validate the MIOL framework, you will need to create your own test data structured as follows

├── data
    ├── test_02
        ├── 00000.jpg
        ├── 00001.jpg
        ├── ...
        ├── 00600.jpg
        ├── calib.yaml
    ├── test_03
        ├── 00000.jpg
        ├── 00001.jpg
        ├── ...
        ├── 00600.jpg

☀️ Setup

We recommend using Anaconda to set up an environment

cd MIOL
conda create -n pytorch python=3.7
conda activate pytorch
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu117
pip install -r requirements.txt

Please modify the torch version based on your GPU. We managed to test our code on:

• Ubuntu 18.04/20.04 with Python 3.7 and CUDA 11.1.
• Windows 10/11 with Python 3.7 and CUDA 11.7.

🚴 Training

Pretrained models can be downloaded from OneDrive.

Our model is trained on one RTX-3090 GPU using the following command. Training logs will be written to checkpoints/log_name which can be visualized using tensorboard.

python train_STM_sceneflow_meta.py --meta-batch-size 4 -k 1 -q 1 --inner-lr 1e-4 --meta-lr 1e-4 --epochs 25 --data path_to_SceneFlowSubData/ --name log_name

🎥 Demos

You can demo the trained model on a sequence of stereo images. To predict depth for your dataset, run

python test_inference.py --pretrained-model sceneflow_pretrained.tar --calib-path path_to_calib_yaml --dataset-dir path_to_test_02 --output-dir output --output-depth

To save the depth values as .npy files and the depth maps as .png images, run with the --output-depth flag.

🍬 Visualization

Here we show some results of the proposed MIOL framework on Hamlyn and SCARED datasets.

• Depth map predictions

• Point Clouds

1.mp4

🌹 Acknowledgment

Our code is based on the excellent works of SC-SfMLearner and monodepth2.

License

For academic usage, the code is released under the permissive MIT license. Our intension of sharing the project is for research/personal purpose. For any commercial purpose, please contact the authors.

Citation

If you find this code useful for your research, please use the following BibTeX entries:

@article{zheng2024MIOL,
  author={Zheng, Jieyu and Li, Xiaojian and Wang, Xin and Wu, Haojun and Li, Ling and Ma, Xiang and Yang, Shanlin},
  journal={IEEE Transactions on Instrumentation and Measurement}, 
  title={Real-Time and High-Accuracy Switchable Stereo Depth Estimation Method Utilizing Self-Supervised Online Learning Mechanism for MIS}, 
  year={2024},
  volume={73},
  pages={1-13}
}