Learned Multi-Patch Similarity

Wilfried Hartmann; Silvano Galliani; Michal Havlena; Luc Van Gool; Konrad Schindler

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arXiv · 1703.08836

Learned Multi-Patch Similarity

Wilfried Hartmann·Silvano Galliani·Michal Havlena·Luc Van Gool·Konrad Schindler

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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.

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Wilfried Hartmann, Silvano Galliani, Michal Havlena, Luc Van Gool, Konrad Schindler. 2017-08-21. Learned Multi-Patch Similarity. https://arxiv.org/abs/1703.08836

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