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Erschienen in:
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

RepMatch: Robust Feature Matching and Pose for Reconstructing Modern Cities

verfasst von : Wen-Yan Lin, Siying Liu, Nianjuan Jiang, Minh. N. Do, Ping Tan, Jiangbo Lu

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

A perennial problem in recovering 3-D models from images is repeated structures common in modern cities. The problem can be traced to the feature matcher which needs to match less distinctive features (permitting wide-baselines and avoiding broken sequences), while simultaneously avoiding incorrect matching of ambiguous repeated features. To meet this need, we develop RepMatch, an epipolar guided (assumes predominately camera motion) feature matcher that accommodates both wide-baselines and repeated structures. RepMatch is based on using RANSAC to guide the training of match consistency curves for differentiating true and false matches. By considering the set of all nearest-neighbor matches, RepMatch can procure very large numbers of matches over wide baselines. This in turn lends stability to pose estimation. RepMatch’s performance compares favorably on standard datasets and enables more complete reconstructions of modern architectures.

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Vorheriges Kapitel DOC: Deep OCclusion Estimation from a Single Image
Nächstes Kapitel Convolutional Oriented Boundaries
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Fußnoten
1
For fine nuances regarding experiment details and comparisons we encourage interested readers to peruse the supplementary material.
 
2
We thank Chin Tat-Jun for his advice on RANSAC comparison.
 
3
We leverage RepMatch’s robust pose estimate to eliminate all triplet poses with relative rotation consistency less than \(2^\circ \). BF was employed with the same scheme.
 
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Metadaten
Titel
RepMatch: Robust Feature Matching and Pose for Reconstructing Modern Cities
verfasst von
Wen-Yan Lin
Siying Liu
Nianjuan Jiang
Minh. N. Do
Ping Tan
Jiangbo Lu
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46447-3

Electronic ISBN: 978-3-319-46448-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46448-0_34