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Domain Siamese CNNs for Sparse Multispectral Disparity Estimation (ICPR 2020)

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Domain Siamese CNNs for Sparse Multispectral Disparity Estimation

This repository contains all the code to reproduce the experiments made in our paper Domain Siamese CNNs for Sparse Multispectral Disparity Estimation accepted at ICPR 2020.

This is an overview of our proposed architecture.

Here are some examples of the disparity maps with the human silhouettes segmented. The segmentation masks were provided with the dataset.

Usage

Dependencies

You can find the dependencies of the project in the requirements.txt file. To install them all, simply type:

pip install -r requirements.txt

Datasets

Download both datasets in a folder named "litiv" on your computer. For the LITIV 2018 dataset, both the rectified images (used for this paper) and the raw images are available.

Train

This is an example of a possible command to train our network.

python train.py --fold 1 --model domainnet

To see all possible options, simply use:

python train.py --help

Test

Here is an example of you want to test the network:

python test.py --fold 1 \ 
               --model domainnet \ 
               --loadmodel pretrained/domainnet/fold1.pt \ 
               --max_disparity 64 \
               --n 3

Once again, to see all available options, use:

python test.py --help

Contact

For any comments, questions or concerns, feel free to contact me at david-alexandre.beaupre@polymtl.ca

License

See the LICENSE file for more details.

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Domain Siamese CNNs for Sparse Multispectral Disparity Estimation (ICPR 2020)

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