Rapid 3D registration using local subtree caching in iterative closest point (ICP) algorithm

Uhlenbrock, Ryan; Kim, Kyungnam; Hoffmann, Herbert; Dolne, Jean J.

doi:10.1117/12.2276428

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Additionally, the similarity between two multi-scaled covariance matrix descriptors can be defined using the formula (24) as follow:…”

Section: Feature Matchingmentioning

confidence: 99%

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A multi-scale covariance matrix descriptor and an accurate transformation estimation for robust point cloud registration

Xiong,

Kong,

et al. 2024

Preprint

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This paper presents a robust point cloud registration method based on a multi-scale covariance matrix descriptor and an accurate transformation estimation. Comparing with state-of-the-art feature descriptor such as FPH, 3DSC, Spin Image, etc, our proposed the multi-scale covariance matrix descriptor is superior to deal with registration problem under higher noise environment, which is since mean operation in generating covariance matrix can filters out most of the noise-damaged samples or outliers and also makes itself be robust to noise. Comparing with transformation estimation such as feature matching, clustering, ICP, RANSAC, etc, our transformation estimation is able to find a better optimal transformation between a pair of point clouds which is since our transformation estimation is a multi-level point cloud transformation estimator including feature matching, coarse transformation estimation based on clustering and a fine transformation estimation based on ICP. Experiment findings reveal that our proposed feature descriptor and transformation estimation outperforms state-of-the-art feature descriptors and transformation estimation, and registration effectiveness based on our registration framework of point cloud is extremely successful in Stanford 3D Scanning Repository, SpaceTime dataset, and especially Kinect dataset.

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“…Additionally, the similarity between two multi-scaled covariance matrix descriptors can be defined using the formula (24) as follow:…”

Section: Feature Matchingmentioning

confidence: 99%

“…However, ICP often needs adequate starting transformation settings, and its iteration process is lengthy. As a result, various ICP variations [17][18][19][20][21][22][23][24][25] have been developed to solve these issues.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A multi-scale covariance matrix descriptor and an accurate transformation estimation for robust point cloud registration

Xiong,

Kong,

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…In addition to the quality of the point cloud alignment, the computational efficiency of the method is another critical aspect. Uhlenbrock et al [18] proposed a 2D array based k-d tree to speed up the iterative process. Pavlov et al [19] introduced the Anderson acceleration technique in ICP, helping to reduce the number of iterations required for the method to converge.…”

mentioning

confidence: 99%

Fast and Accurate Registration of Terrestrial Point Clouds Using a Planar Approximation of Roof Features

et al. 2022

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3D reconstructed models are becoming more diffused daily, especially in the Cultural Heritage field. These geometric models are typically obtained from elaborating a 3D point cloud. A significant limit in using these methods is the realignment of different point clouds acquired from different acquisitions, particularly for those whose dimensions are millions of points. Although several methodologies have tried to propose a solution for this necessity, none of these seems to solve definitively the problems related to the realignment of large point clouds. This paper presents a new and innovative procedure for the fine registration of large point clouds. The method performs an alignment by using planar approximations of roof features, taking the roof’s extension into account. It looks particularly suitable for the alignment of large point clouds acquired in urban and archaeological environments. The proposed methodology is compared in terms of accuracy and time with a standard photogrammetric reconstruction based on Ground Control Points (GCPs) and other ones, aligned by the Iterative Closest Point method (ICP) and markers. The results evidence the excellent performance of the methodology, which could represent an alternative for aligning extensive photogrammetric reconstructions without the use of GCPs.

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Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark

Dong

Zang

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

286

109

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Rapid 3D registration using local subtree caching in iterative closest point (ICP) algorithm

Cited by 4 publications

References 5 publications

A multi-scale covariance matrix descriptor and an accurate transformation estimation for robust point cloud registration

A multi-scale covariance matrix descriptor and an accurate transformation estimation for robust point cloud registration

Fast and Accurate Registration of Terrestrial Point Clouds Using a Planar Approximation of Roof Features

Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark

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