Efficient large-scale multi-view stereo for ultra high-resolution image sets
| Autor: | Pascal Fua, Christoph Strecha, Engin Tola |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Matching (graph theory)
Computational complexity theory Computer science business.industry 3D reconstruction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Point cloud Scale (descriptive set theory) Computer Science Applications Image (mathematics) Hardware and Architecture Outlier Pattern recognition (psychology) Computer vision Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | Machine Vision and Applications. 23:903-920 |
| ISSN: | 1432-1769 0932-8092 |
| DOI: | 10.1007/s00138-011-0346-8 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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