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• W2128052895 abstract "We present a new approach for large-scale multi-view stereo matching, which is designed to operate on ultra high-resolution image sets and efficiently compute dense 3D point clouds. We show that, using a robust descriptor for matching purposes and high-resolution images, we can skip the computationally expensive steps that other algorithms require. As a result, our method has low memory requirements and low computational complexity while producing 3D point clouds containing virtually no outliers. This makes it exceedingly suitable for large-scale reconstruction. The core of our algorithm is the dense matching of image pairs using DAISY descriptors, implemented so as to eliminate redundancies and optimize memory access. We use a variety of challenging data sets to validate and compare our results against other algorithms." @default.
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• W2128052895 date "2011-05-28" @default.
• W2128052895 modified "2023-10-16" @default.
• W2128052895 title "Efficient large-scale multi-view stereo for ultra high-resolution image sets" @default.
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• W2128052895 doi "https://doi.org/10.1007/s00138-011-0346-8" @default.
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