Learning Scribbles for Dense Depth:Weakly-Supervised Single Underwater Image Depth Estimation Boosted by Multi-Task Learning

[Project] [Paper] [Dataset]

This repository is the official PyTorch implementation of WsUID-Net.

Package requirements

• The following packages are required to run the codes. Note: the version of the packages were the ones we used and are suggestive, not mandatory.
  • python = 3.6
  • opencv-python = 4.5.3
  • torch = 1.10.1
  • easydict = 1.9
  • visdom = 0.1.8.9

Dataset preparation

• You need to prepare datasets for following training and testing activities. You can download SUIM-SDA dataset from Google Drive or Baidu Netdisk.
  • Decompress the SUIM-SDA package to the ./datasets folder
  • You can calculate the edge graph according to the semantic segmentation mask in SUIM-SDA, or download the edge graph directly from here.
• Run ./data/Conver_data.py. Convert the .csv file that stores the depth-rank samples to the .pkl file used for training.

python Conver_data.py

Train

• You need to modify the input and output paths in train.py depending on where your data set is stored on disk, then run the following code in the terminal:

python3 -m visdom.server -port=8007
python train.py 

Test

• You need to change ckpt_path in the test.py file, then run the following code in the terminal:

python test.py 

Using the pre-trained model

You can download the trained model from here. To test on the pre-trained models, change ckpt_path in the test.py file.

Citation

@ARTICLE{10415086,
  author={Li, Kunqian and Wang, Xiya and Liu, Wenjie and Qi, Qi and Hou, Guojia and Zhang, Zhiguo and Sun, Kun},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Learning Scribbles for Dense Depth: Weakly Supervised Single Underwater Image Depth Estimation Boosted by Multitask Learning}, 
  year={2024}
  doi={10.1109/TGRS.2024.3358892}}

Acknowledgements

• https://github.com/xahidbuffon/SUIM