Incremental Rotation Averaging Revisited

Fuente: arXiv
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Auteurs principaux: Gao, Xiang, Cui, Hainan, Liu, Yangdong, Shen, Shuhan
Format: Preprint
Publié: 2023
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author Gao, Xiang
Cui, Hainan
Liu, Yangdong
Shen, Shuhan
author_facet Gao, Xiang
Cui, Hainan
Liu, Yangdong
Shen, Shuhan
contents In order to further advance the accuracy and robustness of the incremental parameter estimation-based rotation averaging methods, in this paper, a new member of the Incremental Rotation Averaging (IRA) family is introduced, which is termed as IRAv4. As its most significant feature, a task-specific connected dominating set is extracted in IRAv4 to serve as a more reliable and accurate reference for rotation local-to-global alignment. This alignment reference is incrementally constructed, together with the absolute rotations of the vertices belong to it simultaneously estimated. Comprehensive evaluations are performed on the 1DSfM dataset, by which the effectiveness of both the reference construction method and the entire rotation averaging pipeline proposed in this paper is demonstrated.
format Preprint
id arxiv_https___arxiv_org_abs_2309_16924
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Incremental Rotation Averaging Revisited
Gao, Xiang
Cui, Hainan
Liu, Yangdong
Shen, Shuhan
Computer Vision and Pattern Recognition
In order to further advance the accuracy and robustness of the incremental parameter estimation-based rotation averaging methods, in this paper, a new member of the Incremental Rotation Averaging (IRA) family is introduced, which is termed as IRAv4. As its most significant feature, a task-specific connected dominating set is extracted in IRAv4 to serve as a more reliable and accurate reference for rotation local-to-global alignment. This alignment reference is incrementally constructed, together with the absolute rotations of the vertices belong to it simultaneously estimated. Comprehensive evaluations are performed on the 1DSfM dataset, by which the effectiveness of both the reference construction method and the entire rotation averaging pipeline proposed in this paper is demonstrated.
title Incremental Rotation Averaging Revisited
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2309.16924