Learned Multi-Patch Similarity
Abstract
Estimating a depth map from multiple views of a scene is a fundamental task in computer vision. As soon as more than two viewpoints are available, one faces the very basic question how to measure similarity across >2 image patches. Surprisingly, no direct solution exists, instead it is common to fall back to more or less robust averaging of two-view similarities. Encouraged by the success of machine learning, and in particular convolutional neural networks, we propose to learn a matching function which directly maps multiple image patches to a scalar similarity score. Experiments on several multi-view datasets demonstrate that this approach has advantages over methods based on pairwise patch similarity.
Cite
Text
Hartmann et al. "Learned Multi-Patch Similarity." International Conference on Computer Vision, 2017. doi:10.1109/ICCV.2017.176Markdown
[Hartmann et al. "Learned Multi-Patch Similarity." International Conference on Computer Vision, 2017.](https://mlanthology.org/iccv/2017/hartmann2017iccv-learned/) doi:10.1109/ICCV.2017.176BibTeX
@inproceedings{hartmann2017iccv-learned,
title = {{Learned Multi-Patch Similarity}},
author = {Hartmann, Wilfried and Galliani, Silvano and Havlena, Michal and Van Gool, Luc and Schindler, Konrad},
booktitle = {International Conference on Computer Vision},
year = {2017},
doi = {10.1109/ICCV.2017.176},
url = {https://mlanthology.org/iccv/2017/hartmann2017iccv-learned/}
}