MCCNN 1.4.0a1

MCCNN is a neural network for learning a similarity measure on image patches

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 from Pypi

MCCNN is available on Pypi and can be installed by:

pip install MCCNN

Developer install

After cloning source code from repository, do a local pip install in a virtualenv through MCCNN Makefile:

make install

PyTorch is an optional dependency. If your work requires it (e.g. training or running neural networks, or loading .pt weight files), install the torch extras group:

make install-torch

or

pip install .[torch]

Note: If you do not need PyTorch, the base make install is sufficient.

Usage

Documentation explains how to train and use MCCNN convolutional neural network. To generate it, please execute the following commands:

make docs

Let's see pandora_plugin_mccnn for real life example.

Pretrained Weights for MCCNN networks

Download weights files

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 PyTorch 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 PyTorch weights of the pretrained networks on the Data Fusion Contest dataset [DFC]
• mc_cnn_fast_int8_excl_01.onnx and mc_cnn_fast_dw.onnx are the ONNX weights of the pretrained networks on the Middlebury dataset [Middlebury]

To download the pretrained weights:

wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/src/mc_cnn/weights/mc_cnn_fast_mb_weights.pt
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/src/mc_cnn/weights/mc_cnn_fast_data_fusion_contest.pt
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/src/mc_cnn/weights/mc_cnn_accurate_mb_weights.pt
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/src/mc_cnn/weights/mc_cnn_accurate_data_fusion_contest.pt
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/src/mc_cnn/weights/mc_cnn_fast_int8_excl_01.onnx
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/src/mc_cnn/weights/mc_cnn_fast_dw.onnx

Access weights from pip package

Pretrained weights are stored into the pip package and downloaded for any installation of mc_cnn pip package. To access it, use the weights submodule :

from mc_cnn.weights import get_weights, get_onnx
mc_cnn_fast_mb_weights_path = get_weights(arch="fast", training_dataset="middlebury")
mc_cnn_fast_data_fusion_contest_path = get_weights(arch="fast", training_dataset="dfc")
mc_cnn_accurate_mb_weights_path = get_weights(arch="accurate", training_dataset="middlebury")
mc_cnn_accurate_data_fusion_contest = get_weights(arch="accurate", training_dataset="dfc")
onnx_mc_cnn_fast_int_8_mb_weights_path = get_onnx(arch="onnx_fast_int8", training_dataset="middlebury")
onnx_mc_cnn_fast_dw_mb_weights_path = get_onnx(arch="onnx_fast_dw", training_dataset="middlebury")

References

Please cite the following paper when using MCCNN:

Defonte, V., Dumas, L., Cournet, M., & Sarrazin, E. (2021, July). Evaluation of MC-CNN Based Stereo Matching Pipeline for the CO3D Earth Observation Program. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 7670-7673). IEEE.

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.