Model based pose estimation using SURF

Authors:
Peter Decker;Dietrich Paulus
Affiliations:
Active Vision Group, University of Koblenz-Landau, Koblenz, Germany;Active Vision Group, University of Koblenz-Landau, Koblenz, Germany
Venue:
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Year:
2010

Citing 9
Cited 0

Least-Squares Estimation of Transformation Parameters Between Two Point Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Efficient Linear Solution of Exterior Orientation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Image Based Localization in Urban Environments

3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Speeded-Up Robust Features (SURF)

Computer Vision and Image Understanding
Modeling the World from Internet Photo Collections

International Journal of Computer Vision
SBA: A software package for generic sparse bundle adjustment

ACM Transactions on Mathematical Software (TOMS)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Estimation of a camera pose (position and orientation) from an image, given a 3d model of the world, is a topic of great interest in many current fields of research. When aiming for a model based pose estimation approach, several questions arise: What is the model? How do we acquire a model? How is the image linked to the model? How is a pose computed and verified using the latter information? In this paper we present a new approach towards model based pose estimation based solely on SURF features. We give a formal definition of our model, show how to build such a model from image data automatically, how to integrate two partial models, and how pose estimation for new images works.