Point cloud registration algorithm based on curvature feature similarity

Yao, Zongwei; Zhao, Quanxiao; Li, Xuefei; Bi, Qiushi

doi:10.1016/j.measurement.2021.109274

Cited by 54 publications

(15 citation statements)

References 18 publications

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“…In [ 30 ], considering the limitations of classical ICP approaches, this paper proposes a improved iterative nearest point (ICP) approach by the similarity of point cloud curvature features. Based on the classical ICP algorithm, the authors introduce the rough alignment method of principal component analysis, and use k-D tree to segment three-dimensional point cloud to fasten the search process of nearest neighbor points.…”

Section: Related Literaturementioning

confidence: 99%

Lightweight similarity checking for English literatures in mobile edge computing

et al. 2023

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With the advent of information age, mobile devices have become one of the major convenient equipment that aids people’s daily office activities such as academic research, one of whose major tasks is to check the repetition rate or similarity among different English literatures. Traditional literature similarity checking solutions in cloud paradigm often call for intensive computational cost and long waiting time. To tackle this issue, in this paper, we modify the traditional literature similarity checking solution in cloud paradigm to make it suitable for the light-weight mobile edge environment. Furthermore, we put forward a lightweight similarity checking approach $$\mathrm {SC_{MEC}}$$ SC MEC for English literatures in mobile edge computing environment. To validate the advantages of $$\mathrm {SC_{MEC}}$$ SC MEC , we have designed massive experiments on a dataset. The reported experimental results show that $$\mathrm {SC_{MEC}}$$ SC MEC can deliver a satisfactory similarity checking result of literatures compared to other existing approaches.

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Section: Related Literaturementioning

confidence: 99%

Lightweight similarity checking for English literatures in mobile edge computing

et al. 2023

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“…The method explains the coherent drift in the variational Bayesian inference theory, while keeping the fundamental features of the CPD algorithm. Recently, a rigid ICP based registration algorithm was presented in [27] which uses curvature feature similarity to find more accurate correspondences. However, the method is sensitive to the noise, and the exponential growth of the computing time regarding the number of points in the surfaces also prevents its wide application to more complicated geometries.…”

Section: A Correspondencesmentioning

confidence: 99%

Feature Preserving Non-Rigid Iterative Weighted Closest Point and Semi-Curvature Registration

Tajdari

Huysmans

Yang

et al. 2022

IEEE Trans. on Image Process.

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“…The accuracy of 3D modeling is determined by the registration algorithm, as a classical 3D point cloud registration algorithm, the iterative closest point (ICP) algorithm has been widely used in plant modeling [ 38 , 53 ]. It is difficult to establish an accurate plant 3D model based on the information collected from one view, so it is necessary to scan the target from multiple views to obtain point clouds in different directions and integrate them effectively [ 54 ]. Point clouds from more perspectives will improve the accuracy of modeling, but the more point clouds for registration, the longer the time required to establish the model, and the modeling efficiency can decrease as the number of points increases [ 55 ].…”

Section: Introductionmentioning

confidence: 99%

Fast reconstruction method of three-dimension model based on dual RGB-D cameras for peanut plant

et al. 2023

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Background Plant shape and structure are important factors in peanut breeding research. Constructing a three-dimension (3D) model can provide an effective digital tool for comprehensive and quantitative analysis of peanut plant structure. Fast and accurate are always the goals of the plant 3D model reconstruction research. Results We proposed a 3D reconstruction method based on dual RGB-D cameras for the peanut plant 3D model quickly and accurately. The two Kinect v2 were mirror symmetry placed on both sides of the peanut plant, and the point cloud data obtained were filtered twice to remove noise interference. After rotation and translation based on the corresponding geometric relationship, the point cloud acquired by the two Kinect v2 was converted to the same coordinate system and spliced into the 3D structure of the peanut plant. The experiment was conducted at various growth stages based on twenty potted peanuts. The plant traits’ height, width, length, and volume were calculated through the reconstructed 3D models, and manual measurement was also carried out during the experiment processing. The accuracy of the 3D model was evaluated through a synthetic coefficient, which was generated by calculating the average accuracy of the four traits. The test result showed that the average accuracy of the reconstructed peanut plant 3D model by this method is 93.42%. A comparative experiment with the iterative closest point (ICP) algorithm, a widely used 3D modeling algorithm, was additionally implemented to test the rapidity of this method. The test result shows that the proposed method is 2.54 times faster with approximated accuracy compared to the ICP method. Conclusions The reconstruction method for the 3D model of the peanut plant described in this paper is capable of rapidly and accurately establishing a 3D model of the peanut plant while also meeting the modeling requirements for other species' breeding processes. This study offers a potential tool to further explore the 3D model for improving traits and agronomic qualities of plants.

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Point cloud registration algorithm based on curvature feature similarity

Cited by 54 publications

References 18 publications

Lightweight similarity checking for English literatures in mobile edge computing

Lightweight similarity checking for English literatures in mobile edge computing

Feature Preserving Non-Rigid Iterative Weighted Closest Point and Semi-Curvature Registration

Fast reconstruction method of three-dimension model based on dual RGB-D cameras for peanut plant

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