STRUCTURELESS BUNDLE ADJUSTMENT WITH SELF-CALIBRATION USING ACCUMULATED CONSTRAINTS
Bundle adjustment based on collinearity is the most widely used optimization method within image based scene reconstruction. It incorporates observed image coordinates, exterior and intrinsic camera parameters as well as object space coordinates of the observed points. The latter dominate the result...
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Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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doaj-8fd9de0ee8134bebaf1a9a8f776323762020-11-24T21:45:06ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502016-06-01III-33910.5194/isprs-annals-III-3-3-2016STRUCTURELESS BUNDLE ADJUSTMENT WITH SELF-CALIBRATION USING ACCUMULATED CONSTRAINTSA. Cefalu0N. Haala1D. Fritsch2Institute for Photogrammetry, University of Stuttgart, Stuttgart, GermanyInstitute for Photogrammetry, University of Stuttgart, Stuttgart, GermanyInstitute for Photogrammetry, University of Stuttgart, Stuttgart, GermanyBundle adjustment based on collinearity is the most widely used optimization method within image based scene reconstruction. It incorporates observed image coordinates, exterior and intrinsic camera parameters as well as object space coordinates of the observed points. The latter dominate the resulting nonlinear system, in terms of the number of unknowns which need to be estimated. In order to reduce the size of the problem regarding memory footprint and computational effort, several approaches have been developed to make the process more efficient, e.g. by exploitation of sparsity or hierarchical subdivision. Some recent developments express the bundle problem through epipolar geometry and scale consistency constraints which are free of object space coordinates. These approaches are usually referred to as structureless bundle adjustment. The number of unknowns in the resulting system is drastically reduced. However, most work in this field is focused on optimization towards speed and considers calibrated cameras, only. We present our work on structureless bundle adjustment, focusing on precision issues as camera calibration and residual weighting. We further investigate accumulation of constraint residuals as an approach to decrease the number of rows of the Jacobian matrix.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-3/3/2016/isprs-annals-III-3-3-2016.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
A. Cefalu N. Haala D. Fritsch |
spellingShingle |
A. Cefalu N. Haala D. Fritsch STRUCTURELESS BUNDLE ADJUSTMENT WITH SELF-CALIBRATION USING ACCUMULATED CONSTRAINTS ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
A. Cefalu N. Haala D. Fritsch |
author_sort |
A. Cefalu |
title |
STRUCTURELESS BUNDLE ADJUSTMENT WITH SELF-CALIBRATION USING ACCUMULATED CONSTRAINTS |
title_short |
STRUCTURELESS BUNDLE ADJUSTMENT WITH SELF-CALIBRATION USING ACCUMULATED CONSTRAINTS |
title_full |
STRUCTURELESS BUNDLE ADJUSTMENT WITH SELF-CALIBRATION USING ACCUMULATED CONSTRAINTS |
title_fullStr |
STRUCTURELESS BUNDLE ADJUSTMENT WITH SELF-CALIBRATION USING ACCUMULATED CONSTRAINTS |
title_full_unstemmed |
STRUCTURELESS BUNDLE ADJUSTMENT WITH SELF-CALIBRATION USING ACCUMULATED CONSTRAINTS |
title_sort |
structureless bundle adjustment with self-calibration using accumulated constraints |
publisher |
Copernicus Publications |
series |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
2194-9042 2194-9050 |
publishDate |
2016-06-01 |
description |
Bundle adjustment based on collinearity is the most widely used optimization method within image based scene reconstruction. It incorporates observed image coordinates, exterior and intrinsic camera parameters as well as object space coordinates of the observed points. The latter dominate the resulting nonlinear system, in terms of the number of unknowns which need to be estimated. In order to reduce the size of the problem regarding memory footprint and computational effort, several approaches have been developed to make the process more efficient, e.g. by exploitation of sparsity or hierarchical subdivision. Some recent developments express the bundle problem through epipolar geometry and scale consistency constraints which are free of object space coordinates. These approaches are usually referred to as structureless bundle adjustment. The number of unknowns in the resulting system is drastically reduced. However, most work in this field is focused on optimization towards speed and considers calibrated cameras, only. We present our work on structureless bundle adjustment, focusing on precision issues as camera calibration and residual weighting. We further investigate accumulation of constraint residuals as an approach to decrease the number of rows of the Jacobian matrix. |
url |
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-3/3/2016/isprs-annals-III-3-3-2016.pdf |
work_keys_str_mv |
AT acefalu structurelessbundleadjustmentwithselfcalibrationusingaccumulatedconstraints AT nhaala structurelessbundleadjustmentwithselfcalibrationusingaccumulatedconstraints AT dfritsch structurelessbundleadjustmentwithselfcalibrationusingaccumulatedconstraints |
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1725906625211924480 |