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.
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Tola, E., Strecha, C. & Fua, P. Efficient large-scale multi-view stereo for ultra high-resolution image sets. Machine Vision and Applications 23, 903–920 (2012). https://doi.org/10.1007/s00138-011-0346-8
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DOI: https://doi.org/10.1007/s00138-011-0346-8