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A Fast Weighted Registration Method of 3D Point Cloud Based on Curvature Feature

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Bin LiangAuthors Info & Claims

ICMIP '18: Proceedings of the 3rd International Conference on Multimedia and Image Processing

Pages 83 - 87

https://doi.org/10.1145/3195588.3195595

Published: 16 March 2018 Publication History

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Abstract

In order to realize the fast and accurate registration of 3D point cloud data, a new fast weighted registration method is proposed in this paper. Firstly, using curvature feature, the method samples the original 3D point cloud data to quickly find matching points and remove wrong point pairs. Secondly, by introducing the iterative re-weighted least squares (IRLS) algorithm, the method carries out coarse alignment of the scattered point cloud. Finally, the method presents an improved distance-weighted Iterative Closest Point (ICP) algorithm to achieve fine matching. The experimental results show that the method has good convergence, robustness and accuracy.

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Cited By

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He LLi WGuan YZhang H(2024)IGICP: Intensity and Geometry Enhanced LiDAR OdometryIEEE Transactions on Intelligent Vehicles10.1109/TIV.2023.33363769:1(541-554)Online publication date: Jan-2024
https://doi.org/10.1109/TIV.2023.3336376
Chu GFan DDong YJi SGu LLi DZhang W(2023)Robust registration of aerial and close‐range photogrammetric point clouds using visual context features and scale consistencyIET Image Processing10.1049/ipr2.1282117:9(2698-2709)Online publication date: 15-May-2023
https://doi.org/10.1049/ipr2.12821
Dong CNewman T(2022)A PCA-aided EV-EGI Method for Registering Volumetric DatasetsProceedings of the 2022 7th International Conference on Multimedia and Image Processing10.1145/3517077.3517095(110-117)Online publication date: 14-Jan-2022
https://dl.acm.org/doi/10.1145/3517077.3517095
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Index Terms

A Fast Weighted Registration Method of 3D Point Cloud Based on Curvature Feature
1. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

ICMIP '18: Proceedings of the 3rd International Conference on Multimedia and Image Processing

March 2018

125 pages

ISBN:9781450364683

DOI:10.1145/3195588

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Wuhan Univ.: Wuhan University, China
University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2018

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ICMIP 2018

ICMIP 2018: 2018 the 3rd International Conference on Multimedia and Image Processing

March 16 - 18, 2018

Guiyang, China

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Cited By

View all

He LLi WGuan YZhang H(2024)IGICP: Intensity and Geometry Enhanced LiDAR OdometryIEEE Transactions on Intelligent Vehicles10.1109/TIV.2023.33363769:1(541-554)Online publication date: Jan-2024
https://doi.org/10.1109/TIV.2023.3336376
Chu GFan DDong YJi SGu LLi DZhang W(2023)Robust registration of aerial and close‐range photogrammetric point clouds using visual context features and scale consistencyIET Image Processing10.1049/ipr2.1282117:9(2698-2709)Online publication date: 15-May-2023
https://doi.org/10.1049/ipr2.12821
Dong CNewman T(2022)A PCA-aided EV-EGI Method for Registering Volumetric DatasetsProceedings of the 2022 7th International Conference on Multimedia and Image Processing10.1145/3517077.3517095(110-117)Online publication date: 14-Jan-2022
https://dl.acm.org/doi/10.1145/3517077.3517095
Chaabane MKoller B(2022)A Systematic Literature Review on Multi-modal Medical Image RegistrationService-Oriented Computing – ICSOC 2022 Workshops10.1007/978-3-031-26507-5_8(97-105)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-26507-5_8
Marchel ŁSpecht CSpecht M(2020)Testing the Accuracy of the Modified ICP Algorithm with Multimodal Weighting FactorsEnergies10.3390/en1322593913:22(5939)Online publication date: 13-Nov-2020
https://doi.org/10.3390/en13225939

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An innovative segmentation method with multi-feature fusion for 3D point cloud

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FFA-Net: fast feature aggregation network for 3D point cloud segmentation