MCCNN is a neural network for learning a similarity measure on image patches
Project description
MCCNN
MCCNN neural network for stereo matching cost.
Overview • Install • Usage • Pretrained Weights for MCCNN networks • Related • References
Overview
Pytorch implementation of [MCCNN] neural network which computes a similarity measure on pair of small image patches.
Install
MCCNN is available on Pypi and can be installed by:
pip install MCCNN
Usage
Documentation explains how to train and use MCCNN convolutional neural network. To generate it, please execute the following commands:
pip install MCCNN[doc]
python setup.py build_sphinx
Let's see pandora_plugin_mccnn for real life example.
Pretrained Weights for MCCNN networks
Pretrained weights for mc-cnn fast and mc-cnn accurate neural networks are available in the weights directory :
- mc_cnn_fast_mb_weights.pt and mc_cnn_accurate_mb_weights.pt are the weights of the pretrained networks on the Middlebury dataset [Middlebury]
- mc_cnn_fast_data_fusion_contest.pt and mc_cnn_accurate_data_fusion_contest.pt are the weights of the pretrained networks on the Data Fusion Contest dataset [DFC]
To download the pretrained weights:
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/weights/mc_cnn_fast_mb_weights.pt
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/weights/mc_cnn_fast_data_fusion_contest.pt
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/weights/mc_cnn_accurate_mb_weights.pt
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/weights/mc_cnn_accurate_data_fusion_contest.pt
References
Please cite the following paper when using MCCNN:
Cournet, M., Sarrazin, E., Dumas, L., Michel, J., Guinet, J., Youssefi, D., Defonte, V., Fardet, Q., 2020. Ground-truth generation and disparity estimation for optical satellite imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
[MCCNN] Zbontar, J., & LeCun, Y. (2016). Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res., 17(1), 2287-2318.
[Middlebury] Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., & Westling, P. (2014, September). High-resolution stereo datasets with subpixel-accurate ground truth. In German conference on pattern recognition (pp. 31-42). Springer, Cham.
[DFC] Bosch, M., Foster, K., Christie, G., Wang, S., Hager, G. D., & Brown, M. (2019, January). Semantic stereo for incidental satellite images. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1524-1532). IEEE.
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