Academic Journal
Efficient and distributed large-scale point cloud bundle adjustment via majorization-minimization.
| Title: | Efficient and distributed large-scale point cloud bundle adjustment via majorization-minimization. |
|---|---|
| Authors: | Li, Rundong1, Liu, Zheng1, Wei, Hairuo1, Cai, Yixi1, Li, Haotian1, Zhang, Fu1 fuzhang@hku.hk |
| Source: | International Journal of Robotics Research. Sep2026, Vol. 45 Issue 10, p1539-1566. 28p. |
| Subject Terms: | *Distributed computing, *Mathematical optimization, Point cloud, Cartography, Surrogate-based optimization, Computer memory management, Computational complexity |
| Abstract: | Point cloud bundle adjustment is critical in large-scale point cloud mapping. However, point cloud bundle adjustment is both computationally and memory intensive, growing dramatically as the number of scan poses increases. This paper presents BALM3.0, an efficient and distributed large-scale point cloud bundle adjustment method. The proposed method employs the majorization-minimization algorithm to decouple the scan poses in the bundle adjustment process, thus performing the point cloud bundle adjustment on large-scale data with improved computational efficiency. The key difficulty of applying majorization-minimization on bundle adjustment is to identify the proper upper surrogate cost function. In this paper, the proposed upper surrogate cost function is based on the point-to-plane distance. The primary advantages of decoupling the scan poses via a majorization-minimization algorithm stem from two key aspects. First, the decoupling of scan poses reduces the optimization time complexity from cubic to linear, significantly enhancing the computational efficiency of the bundle adjustment process in large-scale environments. Second, it lays the theoretical foundation for distributed bundle adjustment. By distributing both data and computation across multiple devices, this approach helps overcome the limitations posed by large memory and computational requirements, which may be difficult for a single device to handle. The proposed method is extensively evaluated in both simulated and real-world environments. The results demonstrate that the proposed method achieves the same optimal residual with comparable accuracy while offering up to 704 times faster optimization speed and reducing memory usage to 1/8. Furthermore, this paper also presented and implemented a distributed bundle adjustment framework and successfully optimized large-scale data (21,436 poses with 70 GB point clouds) with four consumer-level laptops. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Robotics Research is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Efficient and distributed large-scale point cloud bundle adjustment via majorization-minimization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Rundong%22">Li, Rundong</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zheng%22">Liu, Zheng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wei%2C+Hairuo%22">Wei, Hairuo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Cai%2C+Yixi%22">Cai, Yixi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+Haotian%22">Li, Haotian</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Fu%22">Zhang, Fu</searchLink><relatesTo>1</relatesTo><i> fuzhang@hku.hk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Robotics+Research%22">International Journal of Robotics Research</searchLink>. Sep2026, Vol. 45 Issue 10, p1539-1566. 28p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Point+cloud%22">Point cloud</searchLink><br /><searchLink fieldCode="DE" term="%22Cartography%22">Cartography</searchLink><br /><searchLink fieldCode="DE" term="%22Surrogate-based+optimization%22">Surrogate-based optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+memory+management%22">Computer memory management</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Point cloud bundle adjustment is critical in large-scale point cloud mapping. However, point cloud bundle adjustment is both computationally and memory intensive, growing dramatically as the number of scan poses increases. This paper presents BALM3.0, an efficient and distributed large-scale point cloud bundle adjustment method. The proposed method employs the majorization-minimization algorithm to decouple the scan poses in the bundle adjustment process, thus performing the point cloud bundle adjustment on large-scale data with improved computational efficiency. The key difficulty of applying majorization-minimization on bundle adjustment is to identify the proper upper surrogate cost function. In this paper, the proposed upper surrogate cost function is based on the point-to-plane distance. The primary advantages of decoupling the scan poses via a majorization-minimization algorithm stem from two key aspects. First, the decoupling of scan poses reduces the optimization time complexity from cubic to linear, significantly enhancing the computational efficiency of the bundle adjustment process in large-scale environments. Second, it lays the theoretical foundation for distributed bundle adjustment. By distributing both data and computation across multiple devices, this approach helps overcome the limitations posed by large memory and computational requirements, which may be difficult for a single device to handle. The proposed method is extensively evaluated in both simulated and real-world environments. The results demonstrate that the proposed method achieves the same optimal residual with comparable accuracy while offering up to 704 times faster optimization speed and reducing memory usage to 1/8. Furthermore, this paper also presented and implemented a distributed bundle adjustment framework and successfully optimized large-scale data (21,436 poses with 70 GB point clouds) with four consumer-level laptops. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Robotics Research is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/02783649251398874 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 1539 Subjects: – SubjectFull: Distributed computing Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Point cloud Type: general – SubjectFull: Cartography Type: general – SubjectFull: Surrogate-based optimization Type: general – SubjectFull: Computer memory management Type: general – SubjectFull: Computational complexity Type: general Titles: – TitleFull: Efficient and distributed large-scale point cloud bundle adjustment via majorization-minimization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Rundong – PersonEntity: Name: NameFull: Liu, Zheng – PersonEntity: Name: NameFull: Wei, Hairuo – PersonEntity: Name: NameFull: Cai, Yixi – PersonEntity: Name: NameFull: Li, Haotian – PersonEntity: Name: NameFull: Zhang, Fu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02783649 Numbering: – Type: volume Value: 45 – Type: issue Value: 10 Titles: – TitleFull: International Journal of Robotics Research Type: main |
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