Non-Destructive Measurement of Three-Dimensional Plants Based on Point Cloud

Wang, Yawei; Chen, Yifei

doi:10.3390/plants9050571

Cited by 22 publications

(20 citation statements)

References 37 publications

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“…where D n×m−1 denotes the distance matrix, D ij is the Euclidean distance between a point and its neighborhood points, and D is the average distance between each point P and its neighborhood points [37]. e values of m and T are set to 50 and 0.5, respectively.…”

Section: Query Object K Imentioning

confidence: 99%

Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data

Qin

Zhou

et al. 2021

Advances in Civil Engineering

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Building information modeling (BIM) in industrialized bridge construction is usually performed based on initial design information. Differences exist between the model of the structure and its actual geometric dimensions and features due to the manufacturing, transportation, hoisting, assembly, and load bearing of the structure. These variations affect the construction project handover and facility management. The solutions available at present entail the use of point clouds to reconstruct BIM. However, these solutions still encounter problems, such as the inability to obtain the actual geometric features of a bridge quickly and accurately. Moreover, the created BIM is nonparametric and cannot be dynamically adjusted. This paper proposes a fully automatic method of reconstructing parameterized BIM by using point clouds to address the abovementioned problems. An algorithm for bridge point cloud segmentation is developed; the algorithm can separate the bridge point cloud from the entire scanning scene and segment the unit structure point cloud. Another algorithm for extracting the geometric features of the bridge point cloud is also proposed; this algorithm is effective for partially missing point clouds. The feasibility of the proposed method is evaluated and verified using theoretical and actual bridge point clouds, respectively. The reconstruction quality of BIM is also evaluated visually and quantitatively, and the results show that the reconstructed BIM is accurate and reliable.

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Section: Query Object K Imentioning

confidence: 99%

Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data

Qin

Zhou

et al. 2021

Advances in Civil Engineering

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“…Weight, size, and volume are important phenotypic parameters. They can be used not only as an indicator of plant growth vigor but also as a parameter for estimating traits (Wang and Chen, 2020a ; Zevgolis et al, 2021 ). Table grapes are plants with variable spatial structures and complex geometric shapes.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Characteristic Parameters of Grape Clusters Based on Point Cloud Data

Chen

et al. 2022

Front. Plant Sci.

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The measurement of grapevine phenotypic parameters is crucial to quantify crop traits. However, individual differences in grape bunches pose challenges in accurately measuring their characteristic parameters. Hence, this study explores a method for estimating grape feature parameters based on point cloud information: segment the grape point cloud by filtering and region growing algorithm, and register the complete grape point cloud model by the improved iterative closest point algorithm. After estimating model phenotypic size characteristics, the grape bunch surface was reconstructed using the Poisson algorithm. Through the comparative analysis with the existing four methods (geometric model, 3D convex hull, 3D alpha-shape, and voxel-based), the estimation results of the algorithm proposed in this study are the closest to the measured parameters. Experimental data show that the coefficient of determination (R2) of the Poisson reconstruction algorithm is 0.9915, which is 0.2306 higher than the coefficient estimated by the existing alpha-shape algorithm (R2 = 0.7609). Therefore, the method proposed in this study provides a strong basis for the quantification of grape traits.

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“…The registration algorithm can find the relationship between different views by searching the correspondence of key points between multiple views. 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 [41,56]. 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 [57].…”

Section: Introductionmentioning

confidence: 99%

Fast Reconstruction Method of Three-dimension Model Based on Dual RGB-D Cameras for Peanut Plant

Liu

Yuan

Zhao

et al. 2022

Preprint

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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. A 3D reconstruction method based on dual RGB-D cameras was proposed 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 carried out at the same time. 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 shows that the synthetic 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. This approach should be useful for 3D modeling and phenotyping peanut breeding.

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Non-Destructive Measurement of Three-Dimensional Plants Based on Point Cloud

Cited by 22 publications

References 37 publications

Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data

Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data

Estimation of Characteristic Parameters of Grape Clusters Based on Point Cloud Data

Fast Reconstruction Method of Three-dimension Model Based on Dual RGB-D Cameras for Peanut Plant

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