Journal
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
Volume -, Issue -, Pages 1595-1603Publisher
IEEE
DOI: 10.1109/ICCV.2017.176
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Funding
- EU [687757 - REPLICATE]
- NVIDIA Corporation through Academic Hardware Grant
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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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