Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds
| Autor: | Atsuto Maki, Roberto Cipolla, Oliver Woodford, Riccardo Gherardi, Minh-Tri Pham, Frank Perbet, Bjorn Stenger |
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| Rok vydání: | 2013 |
| Předmět: |
Similarity (geometry)
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Point cloud Contrast (statistics) State (functional analysis) Scale invariance Euclidean distance Computer Science::Computer Vision and Pattern Recognition Computer vision Visual Word Artificial intelligence Mean-shift business Mathematics |
| Zdroj: | Machine Learning for Computer Vision ISBN: 9783642286605 |
| DOI: | 10.1007/978-3-642-28661-2_6 |
| Popis: | This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this space—the SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. |
| Databáze: | OpenAIRE |
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