Factorization of Correspondence and Camera Error for Unconstrained Dense Correspondence Applications

Authors:
Daniel Knoblauch;Mauricio Hess-Flores;Mark Duchaineau;Falko Kuester
Affiliations:
University of California, San Diego;University of California, Davis;Lawrence Livermore National Laboratory,;University of California, San Diego
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Year:
2009

Citing 7
Cited 1

In Defense of the Eight-Point Algorithm

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
Bundle Adjustment - A Modern Synthesis

ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Error Analysis of a Real-Time Stereo System

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Visual Modeling with a Hand-Held Camera

International Journal of Computer Vision
An Efficient Solution to the Five-Point Relative Pose Problem

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1

Ray divergence-based bundle adjustment conditioning for multi-view stereo

PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

A correspondence and camera error analysis for dense correspondence applications such as structure from motion is introduced. This provides error introspection, opening up the possibility of adaptively and progressively applying more expensive correspondence and camera parameter estimation methods to reduce these errors. The presented algorithm evaluates the given correspondences and camera parameters based on an error generated through simple triangulation. This triangulation is based on the given dense, non-epipolar constraint, correspondences and estimated camera parameters. This provides an error map without requiring any information about the perfect solution or making assumptions about the scene. The resulting error is a combination of correspondence and camera parameter errors. An simple, fast low/high pass filter error factorization is introduced, allowing for the separation of correspondence error and camera error. Further analysis of the resulting error maps is applied to allow efficient iterative improvement of correspondences and cameras.