Bundle adjustment with implicit structure modeling using a direct linear transform


by H-J Chien, H Geng, R Klette
Abstract:
Bundle adjustment (BA) is considered to be the “golden standard” optimisation technique for multiple-view reconstruction over decades of research. The technique simultaneously tunes camera parameters and scene structure to fit a nonlinear function, in a way that the discrepancy between the observed scene points and their reprojections are minimised in a least-squares manner. Computational feasibility and numerical conditioning are two major concerns of todays BA implementations, and choosing a proper parametrization of structure in 3D space could dramatically improve numerical stability, convergence speed, and cost of evaluating Jacobian matrices. In this paper we study several alternative representations of 3D structure and propose an implicit modeling approach based on a Direct Linear Transform (DLT) estimation. The performances of a variety of parametrization techniques are evaluated using simulated visual odometry scenarios. Experimental results show that the computational cost and convergence speed is further improved to achieve similar accuracy without explicit adjustment over the structure parameters.
Reference:
Bundle adjustment with implicit structure modeling using a direct linear transform (H-J Chien, H Geng, R Klette), In Computer Analysis of Images and Patterns, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, volume 9256, 2015.
Bibtex Entry:
@inproceedings{chien2015bundletransform,
author = "Chien, H-J and Geng, H and Klette, R",
booktitle = "Computer Analysis of Images and Patterns, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
organization = "Valletta, Malta",
pages = "411--422",
publisher = "Springer Verlag",
title = "Bundle adjustment with implicit structure modeling using a direct linear transform",
volume = "9256",
year = "2015",
abstract = "Bundle adjustment (BA) is considered to be the “golden standard” optimisation technique for multiple-view reconstruction over decades of research. The technique simultaneously tunes camera parameters and scene structure to fit a nonlinear function, in a way that the discrepancy between the observed scene points and their reprojections are minimised in a least-squares manner. Computational feasibility and numerical conditioning are two major concerns of todays BA implementations, and choosing a proper parametrization of structure in 3D space could dramatically improve numerical stability, convergence speed, and cost of evaluating Jacobian matrices. In this paper we study several alternative representations of 3D structure and propose an implicit modeling approach based on a Direct Linear Transform (DLT) estimation. The performances of a variety of parametrization techniques are evaluated using simulated visual odometry scenarios. Experimental results show that the computational cost and convergence speed is further improved to achieve similar accuracy without explicit adjustment over the structure parameters.",
doi = "10.1007/978-3-319-23192-1_34",
startyear = "2015",
startmonth = "Sep",
startday = "2",
finishyear = "2015",
finishmonth = "Sep",
finishday = "4",
isbn = "9783319231914",
issn = "0302-9743",
eissn = "1611-3349",
keyword = "Bundle adjustment",
keyword = "Direct linear transform",
keyword = "Multiple view reconstruction",
keyword = "Nonlinear optimization",
language = "eng",
conference = "16th Internatiional conference in Computer Analysis of Images and Patterns",
}