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- W4282979094 abstract "We address the problem of registering synchronized color (RGB) and multi-spectral (MS) images featuring very different resolution by solving stereo matching correspondences. Purposely, we introduce a novel RGB-MS dataset framing 13 different scenes in indoor environments and providing a total of 34 image pairs annotated with semi-dense, high-resolution ground-truth labels in the form of disparity maps. To tackle the task, we propose a deep learning architecture trained in a self-supervised manner by exploiting a further RGB camera, required only during training data acquisition. In this setup, we can conveniently learn cross-modal matching in the absence of ground-truth labels by distilling knowledge from an easier RGB-RGB matching task based on a collection of about 11K unlabeled image triplets. Experiments show that the proposed pipeline sets a good performance bar (1.16 pixels average registration error) for future research on this novel, challenging task." @default.
- W4282979094 created "2022-06-17" @default.
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- W4282979094 date "2022-06-14" @default.
- W4282979094 modified "2023-09-30" @default.
- W4282979094 title "RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation" @default.
- W4282979094 doi "https://doi.org/10.48550/arxiv.2206.07047" @default.
- W4282979094 hasPublicationYear "2022" @default.
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