Improved Feature Point Algorithm for 3D Point Cloud Registration

Kamencay, Patrik; Šinko, Martin; Hudec, Róbert; Benčo, Miroslav; Radil, Roman

doi:10.1109/tsp.2019.8769057

Cited by 16 publications

(13 citation statements)

References 11 publications

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“…He et al [30] combined PointNet++ network and the ICP algorithm for training, and the result of registration was robust with the high speed, but it has unsatisfactory performance on sparse point clouds, because they cannot provide enough features. Kamencay et al [31] use the Scale-Invariant Feature Transform (SIFT) function for the initial alignment transformation of the point clouds, combining with the K-nearest neighbor algorithm and using the weighted ICP algorithm for registration. Aiming to solve problems such as low convergence speed due to uncertainty of initial transformation matrix and difficulty of accurate matching for corresponding points, Xiong et al [32] proposed a novel feature descriptor based on ratio of rotational volume and an improved coarse-to-fine registration pipeline of point clouds, and experimental results show that the improved pairwise registration pipeline is effective in pairwise registration.…”

Section: The Algorithm Of Recognition and Registrationmentioning

confidence: 99%

Matching Algorithm for 3D Point Cloud Recognition and Registration Based on Multi-Statistics Histogram Descriptors

Bing-ren

Meng

et al. 2022

Sensors

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Establishing an effective local feature descriptor and using an accurate key point matching algorithm are two crucial tasks in recognizing and registering on the 3D point cloud. Because the descriptors need to keep enough descriptive ability against the effect of noise, occlusion, and incomplete regions in the point cloud, a suitable key point matching algorithm can get more precise matched pairs. To obtain an effective descriptor, this paper proposes a Multi-Statistics Histogram Descriptor (MSHD) that combines spatial distribution and geometric attributes features. Furthermore, based on deep learning, we developed a new key point matching algorithm that could identify more corresponding point pairs than the existing methods. Our method is evaluated based on Stanford 3D dataset and four real component point cloud dataset from the train bottom. The experimental results demonstrate the superiority of MSHD because its descriptive ability and robustness to noise and mesh resolution are greater than those of carefully selected baselines (e.g., FPFH, SHOT, RoPS, and SpinImage descriptors). Importantly, it has been confirmed that the error of rotation and translation matrix is much smaller based on our key point matching algorithm, and the precise corresponding point pairs can be captured, resulting in enhanced recognition and registration for three-dimensional surface matching.

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Section: The Algorithm Of Recognition and Registrationmentioning

confidence: 99%

Matching Algorithm for 3D Point Cloud Recognition and Registration Based on Multi-Statistics Histogram Descriptors

Bing-ren

Meng

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Classic registration algorithms for point clouds mainly include the iterative closest point (ICP) algorithm [11,12], variants of ICP [13][14][15][16][17][18][19] and geometry-based registration algorithms [20][21][22][23][24].…”

Section: Classic Registration Algorithmsmentioning

confidence: 99%

MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template

Su¹,

Wang²,

Chen³

et al. 2021

Preprint

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With the advancement of photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and was used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain a final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.

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“…Feature-based registration is a classical method used in coarse registration. This method is divided into the following steps: First, key points are selected for use in simplifying the point cloud models with filtering methods, such as the scale invariant feature transform (SIFT) [9] and 3D Harris [10]. Second, the feature descriptors are used to describe the features of points in the point cloud.…”

Section: Related Workmentioning

confidence: 99%

Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

Feng

Tang

et al. 2020

IEEE Access

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In view of the long computation time and low registration accuracy of the current point cloud registration algorithm, a point cloud registration algorithm based on the grey wolf optimizer (GWO) is proposed, denoted PCR-GW. The algorithm uses the centralization method to solve the translation matrix and then simplifies the points of the initial point cloud models by using the intrinsic shape signatures (ISS) feature. Next, various parameters of the rotation matrix are obtained via the GWO algorithm by employing the quadratic sum of the distances between corresponding points in the simplified point cloud as the objective function. Finally, the point cloud registration process is completed by using the obtained transformation matrix. By conducting a registration experiment on the point cloud library model and comparing PCR-GW with the traditional algorithms, the algorithm proposed in this paper is shown to be promising for improving the computation speed and registration accuracy. INDEX TERMS point cloud registration; feature point extraction; grey wolf optimization algorithm

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Improved Feature Point Algorithm for 3D Point Cloud Registration

Cited by 16 publications

References 11 publications

Matching Algorithm for 3D Point Cloud Recognition and Registration Based on Multi-Statistics Histogram Descriptors

Matching Algorithm for 3D Point Cloud Recognition and Registration Based on Multi-Statistics Histogram Descriptors

MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template

Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

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