2L-LSH: A Locality-Sensitive Hash Function-Based Method For Rapid Point Cloud Indexing.
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| Title: | 2L-LSH: A Locality-Sensitive Hash Function-Based Method For Rapid Point Cloud Indexing. |
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| Authors: | Wang, Shurui1, Zhang, Yuhe1 zhangyuhe0601@nwu.edu.cn, Guo, Ruizhe1, Zhang, Yaning1, Xie, Yifei1, Zhou, Xinyu1 zhangyuhe0601@nwu.edu.cn |
| Source: | Computer Journal. Sep2024, Vol. 67 Issue 9, p2809-2818. 10p. |
| Subjects: | Hashing, Algorithms, K-nearest neighbor classification, Radius (Geometry), Visualization |
| Abstract: | The development of 3D scanning technology has enabled the acquisition of massive point cloud models with diverse structures and large scales, thereby presenting significant challenges in point cloud processing. Fast neighboring points search is one of the most common problems, which is frequently used in model reconstruction, classification, retrieval and feature visualization. Hash function is well known for its high-speed and accurate performance in searching high-dimensional data, which is also the core of the proposed 2L-LSH. Specifically, the 2L-LSH algorithm adopts a two-step hash function strategy, in which the popular step divides the bounding box of the point cloud model and the second step constructs a generalized table-based data structure. The proposed 2L-LSH offers a highly efficient and accurate solution for fast neighboring points search in large-scale 3D point cloud models, making it a promising technique for various applications in the field. The proposed algorithm is compared with the well-known methods including Kd-tree and Octree; the obtained results demonstrated that the proposed method outperforms Kd-tree and Octree in terms of speed, i.e. the time consumption of k NN search can be 51.111% and 94.159% lower than Kd-tree and Octree, respectively. And the RN search time can be 54.519% and 41.840% lower than Kd-tree and Octree, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Journal is the property of Oxford University Press / USA 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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 180234010 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: 2L-LSH: A Locality-Sensitive Hash Function-Based Method For Rapid Point Cloud Indexing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Shurui%22">Wang, Shurui</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yuhe%22">Zhang, Yuhe</searchLink><relatesTo>1</relatesTo><i> zhangyuhe0601@nwu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Guo%2C+Ruizhe%22">Guo, Ruizhe</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yaning%22">Zhang, Yaning</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xie%2C+Yifei%22">Xie, Yifei</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Xinyu%22">Zhou, Xinyu</searchLink><relatesTo>1</relatesTo><i> zhangyuhe0601@nwu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Journal%22">Computer Journal</searchLink>. Sep2024, Vol. 67 Issue 9, p2809-2818. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hashing%22">Hashing</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Radius+%28Geometry%29%22">Radius (Geometry)</searchLink><br /><searchLink fieldCode="DE" term="%22Visualization%22">Visualization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The development of 3D scanning technology has enabled the acquisition of massive point cloud models with diverse structures and large scales, thereby presenting significant challenges in point cloud processing. Fast neighboring points search is one of the most common problems, which is frequently used in model reconstruction, classification, retrieval and feature visualization. Hash function is well known for its high-speed and accurate performance in searching high-dimensional data, which is also the core of the proposed 2L-LSH. Specifically, the 2L-LSH algorithm adopts a two-step hash function strategy, in which the popular step divides the bounding box of the point cloud model and the second step constructs a generalized table-based data structure. The proposed 2L-LSH offers a highly efficient and accurate solution for fast neighboring points search in large-scale 3D point cloud models, making it a promising technique for various applications in the field. The proposed algorithm is compared with the well-known methods including Kd-tree and Octree; the obtained results demonstrated that the proposed method outperforms Kd-tree and Octree in terms of speed, i.e. the time consumption of k NN search can be 51.111% and 94.159% lower than Kd-tree and Octree, respectively. And the RN search time can be 54.519% and 41.840% lower than Kd-tree and Octree, respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Journal is the property of Oxford University Press / USA 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: 10.1093/comjnl/bxae047 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 2809 Subjects: – SubjectFull: Hashing Type: general – SubjectFull: Algorithms Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Radius (Geometry) Type: general – SubjectFull: Visualization Type: general Titles: – TitleFull: 2L-LSH: A Locality-Sensitive Hash Function-Based Method For Rapid Point Cloud Indexing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Shurui – PersonEntity: Name: NameFull: Zhang, Yuhe – PersonEntity: Name: NameFull: Guo, Ruizhe – PersonEntity: Name: NameFull: Zhang, Yaning – PersonEntity: Name: NameFull: Xie, Yifei – PersonEntity: Name: NameFull: Zhou, Xinyu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 00104620 Numbering: – Type: volume Value: 67 – Type: issue Value: 9 Titles: – TitleFull: Computer Journal Type: main |
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