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Image-based localization using hybrid feature correspondences

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Author

Summary, in English

Where am I and what am I seeing? This is a classical vision problem and this paper presents a solution based on efficient use of a combination of 2D and 3D features. Given a model of a scene, the objective is to find the relative camera location of a new input image. Unlike traditional hypothesize-and-test methods that try to estimate the unknown camera position based on 3D model features only, or alternatively, based on 2D model features only, we show that using a mixture of such features, that is, a hybrid correspondence set, may improve performance. We use minimal cases of structure-from-motion for hypothesis generation in a RANSAC engine. For this purpose, several new and useful minimal cases are derived for calibrated, semi-calibrated and uncalibrated settings. Based on algebraic geometry methods, we show how these minimal hybrid cases can be solved efficiently. The whole approach has been validated on both synthetic and real data, and we demonstrate improvements compared to previous work. © 2007 IEEE.

Publishing year

2007

Language

English

Pages

2732-2739

Publication/Series

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Mathematics

Keywords

  • Image-based localization
  • Hypothesize-and-test methods
  • Hypothesis generation

Conference name

IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007

Conference date

2007-06-17 - 2007-06-22

Conference place

Minneapolis, MN, United States

Status

Published

ISBN/ISSN/Other

  • ISSN: 1063-6919
  • CODEN: PIVRE9