Learned Multi-Patch Similarity
| Autor: | Hartmann, Wilfried, Galliani, Silvano, Havlena, Michal, Van Gool, Luc, Schindler, Konrad |
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| Rok vydání: | 2017 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 10 pages, 7 figures, Accepted at ICCV 2017 |
| Databáze: | arXiv |
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