@inproceedings{seki2017sgm-nets,
author = {Seki, Akihito and Pollefeys, Marc},
title = {SGM-Nets: Semi-global matching with neural networks},
organization = {IEEE/CVF},
booktitle = {2017 Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2017},
month = {July},
abstract = {This paper deals with deep neural networks for predicting accurate dense disparity map with Semi-global matching (SGM). SGM is a widely used regularization method for real scenes because of its high accuracy and fast computation speed. Even though SGM can obtain accurate results, tuning of SGMs penalty-parameters, which control a smoothness and discontinuity of a disparity map, is uneasy and empirical methods have been proposed. We propose a learning based penalties estimation method, which we call SGM-Nets that consist of Convolutional Neural Networks. A small image patch and its position are input into SGMNets to predict the penalties for the 3D object structures. In order to train the networks, we introduce a novel loss function which is able to use sparsely annotated disparity maps such as captured by a LiDAR sensor in real environments. Moreover, we propose a novel SGM parameterization, which deploys different penalties depending on either positive or negative disparity changes in order to represent the object structures more discriminatively. Our SGM-Nets outperformed state of the art accuracy on KITTI benchmark datasets.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/sgm-nets-semi-global-matching-with-neural-networks/},
}