Learned Multi-Patch Similarity

Wilfried Hartmann, Silvano Galliani, Michal Havlena, Luc Van Gool, Konrad Schindler; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1586-1594


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.

Related Material

[pdf] [supp] [arXiv]
author = {Hartmann, Wilfried and Galliani, Silvano and Havlena, Michal and Van Gool, Luc and Schindler, Konrad},
title = {Learned Multi-Patch Similarity},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}