Journal
MACHINE VISION AND APPLICATIONS
Volume 23, Issue 5, Pages 903-920Publisher
SPRINGER
DOI: 10.1007/s00138-011-0346-8
Keywords
Multi-view stereo; 3D reconstruction; DAISY; High-resolution images
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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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