Multi‐scale point cloud registration based on topological structure

Dong, Tianzhen; Zhao, Yuepeng; Zhang, Qing; Xue, Binghan; Li, Jinghua; Li, Wenju

doi:10.1002/cpe.6873

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…It is practical for real data with unknown orientation. (2) In terms of consuming time, the well-known descriptors, such as FPFH, SHOT, and RoPS, occupy a large amount of data memory [50][51][52]. Meanwhile, most processes in our method are based on 3D key points.…”

Section: The Results Of Experimentsmentioning

confidence: 99%

Establishment and Extension of a Fast Descriptor for Point Cloud Registration

et al. 2022

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Point cloud registration (PCR) is a vital problem in remote sensing and computer vision, which has various important applications, such as 3D reconstruction, object recognition, and simultaneous localization and mapping (SLAM). Although scholars have investigated a variety of methods for PCR, the applications have been limited by low accuracy, high memory footprint, and slow speed, especially for dealing with a large number of point cloud data. To solve these problems, a novel local descriptor is proposed for efficient PCR. We formed a comprehensive description of local geometries with their statistical properties on a normal angle, dot product of query point normal and vector from the point to its neighborhood point, the distance between the query point and its neighborhood point, and curvature variation. Sub-features in descriptors were low-dimensional and computationally efficient. Moreover, we applied the optimized sample consensus (OSAC) algorithm to iteratively estimate the optimum transformation from point correspondences. OSAC is robust and practical for matching highly self-similar features. Experiments and comparisons with the commonly used descriptor were conducted on several synthetic datasets and our real scanned bridge data. The result of the simulation experiments showed that the rotation angle error was below 0.025° and the translation error was below 0.0035 m. The real dataset was terrestrial laser scanning (TLS) data of Sujiaba Bridge in Chongqing, China. The results showed the proposed descriptor successfully registered the practical TLS data with the smallest errors. The experiments demonstrate that the proposed method is fast with high alignment accuracy and achieves a better performance than previous commonly used methods.

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Section: The Results Of Experimentsmentioning

confidence: 99%

Establishment and Extension of a Fast Descriptor for Point Cloud Registration

et al. 2022

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“…In addition, although embedding attention mechanisms in the network can improve the discriminative ability of the feature extractor, sometimes it provides no significant improvement. The other registration methods [23][24][25] also adopt correspondence pair searching methods.…”

Section: Correspondence-based Registration Methodsmentioning

confidence: 99%

SHC: soft-hard correspondences framework for simplifying point cloud registration

Chen,

Yu,

Liu

et al. 2024

EURASIP J. Adv. Signal Process.

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Point cloud registration is a multifaceted problem that involves a series of procedures. Many deep learning methods employ complex structured networks to achieve robust registration performance. However, these intricate structures can amplify the challenges of network learning and impede gradient propagation. To address this concern, the soft-hard correspondence (SHC) framework is introduced in the present paper to streamline the registration problem. The framework encompasses two modes: the hard correspondence mode, which transforms the registration problem into a correspondence pair search problem, and the soft correspondence mode, which addresses this new problem. The simplification of the problem provides two advantages. First, it eliminates the need for intermediate operations that lead to error fusion and counteraction, thereby improving gradient propagation. Second, a perfect solution is not necessary to solve the new problem, since accurate registration results can be achieved even in the presence of errors in the found pairs. The experimental results demonstrate that SHC successfully simplifies the registration problem. It achieves performance comparable to complex networks using a simple network and can achieve zero error on datasets with perfect correspondence pairs.

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MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP

Song,

Zhang,

Luo

et al. 2024

Meas. Sci. Technol.

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The effectiveness of point cloud registration critically determines 3D reconstruction accuracy involving multi-view sensors. We introduce a multi-view point cloud registration method based on Multi-view Spatial Coordinate System ICP to solve the problem of- 3D point cloud registration from different viewpoints. By integrating a spatial rotation axis line, our method successfully establishes the spatial coordinate system tailored for multi-view sensors, ensuring that 3D point clouds derived from various perspectives are optimally positioned initially. We employ the ICP technique for point cloud merging, facilitating a seamless transition from coarse to refined registration of these multi-view 3D point clouds. During the process of spatial rotation axis line fitting, we present a Ransac-based algorithm tailored for axis line fitting that effectively removes outliers, thus significantly improving the fitting precision. Experimental results from a standard sphere reconstruction reveal that within a measurement scope of 1.3 to1.9m, our proposed method boasts a maximum error of just 0.069mm, an average absolute error of 0.039mm, and a root mean square error of 0.043mm. The speed of our point cloud registration outpaces that of alternative methods. Our method notably elevates the precision and velocity of 3D point cloud registration across diverse views, demonstrating commendable adaptability and resilience.

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Multi‐scale point cloud registration based on topological structure

Cited by 3 publications

References 39 publications

Establishment and Extension of a Fast Descriptor for Point Cloud Registration

Establishment and Extension of a Fast Descriptor for Point Cloud Registration

SHC: soft-hard correspondences framework for simplifying point cloud registration

MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP

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