Dataset Information

Geological surface reconstruction from 3D point clouds.

ABSTRACT: The numerical simulation of phenomena such as subsurface fluid flow or rock deformations are based on geological models, where volumes are typically defined through stratigraphic surfaces and faults, which constitute the geometric constraints, and then discretized into blocks to which relevant petrophysical or stress-strain properties are assigned. This paper illustrates the process by which it is possible to reconstruct the triangulation of 3D geological surfaces assigned as point clouds. These geological surfaces can then be used in codes dedicated to volume discretization to generate models of underground rocks. The method comprises the following: - Characterization of the best fitting plane and identification of the concave hull of the point cloud which is projected on it - Triangulation of the point cloud on the plane, constrained to the Planar Straight Line Graph constituted by the concave hull The algorithm, implemented in C++, depends exclusively on two parameters (nDig, maxCut) which allow one to easily evaluate the optimal refinement level of the hull on a case by case basis.

SUBMITTER: Serazio C

PROVIDER: S-EPMC8374526 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Geological surface reconstruction from 3D point clouds.

Serazio Cristina C Tamburini Marco M Verga Francesca F Berrone Stefano S

MethodsX 20210526

The numerical simulation of phenomena such as subsurface fluid flow or rock deformations are based on geological models, where volumes are typically defined through stratigraphic surfaces and faults, which constitute the geometric constraints, and then discretized into blocks to which relevant petrophysical or stress-strain properties are assigned. This paper illustrates the process by which it is possible to reconstruct the triangulation of 3D geological surfaces assigned as point clouds. These ...[more]

PMID: 34430294

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Project description:BackgroundLaserscanning recently has become a powerful and common method for plant parameterization and plant growth observation on nearly every scale range. However, 3D measurements with high accuracy, spatial resolution and speed result in a multitude of points that require processing and analysis. The primary objective of this research has been to establish a reliable and fast technique for high throughput phenotyping using differentiation, segmentation and classification of single plants by a fully automated system. In this report, we introduce a technique for automated classification of point clouds of plants and present the applicability for plant parameterization.ResultsA surface feature histogram based approach from the field of robotics was adapted to close-up laserscans of plants. Local geometric point features describe class characteristics, which were used to distinguish among different plant organs. This approach has been proven and tested on several plant species. Grapevine stems and leaves were classified with an accuracy of up to 98%. The proposed method was successfully transferred to 3D-laserscans of wheat plants for yield estimation. Wheat ears were separated with an accuracy of 96% from other plant organs. Subsequently, the ear volume was calculated and correlated to the ear weight, the kernel weights and the number of kernels. Furthermore the impact of the data resolution was evaluated considering point to point distances between 0.3 and 4.0 mm with respect to the classification accuracy.ConclusionWe introduced an approach using surface feature histograms for automated plant organ parameterization. Highly reliable classification results of about 96% for the separation of grapevine and wheat organs have been obtained. This approach was found to be independent of the point to point distance and applicable to multiple plant species. Its reliability, flexibility and its high order of automation make this method well suited for the demands of high throughput phenotyping.Highlights• Automatic classification of plant organs using geometrical surface information• Transfer of analysis methods for low resolution point clouds to close-up laser measurements of plants• Analysis of 3D-data requirements for automated plant organ classification.

Project description:The phenotyping of Pinus massoniana seedlings is essential for breeding, vegetation protection, resource investigation, and so on. Few reports regarding estimating phenotypic parameters accurately in the seeding stage of Pinus massoniana plants using 3D point clouds exist. In this study, seedlings with heights of approximately 15-30 cm were taken as the research object, and an improved approach was proposed to automatically calculate five key parameters. The key procedure of our proposed method includes point cloud preprocessing, stem and leaf segmentation, and morphological trait extraction steps. In the skeletonization step, the cloud points were sliced in vertical and horizontal directions, gray value clustering was performed, the centroid of the slice was regarded as the skeleton point, and the alternative skeleton point of the main stem was determined by the DAG single source shortest path algorithm. Then, the skeleton points of the canopy in the alternative skeleton point were removed, and the skeleton point of the main stem was obtained. Last, the main stem skeleton point after linear interpolation was restored, while stem and leaf segmentation was achieved. Because of the leaf morphological characteristics of Pinus massoniana, its leaves are large and dense. Even using a high-precision industrial digital readout, it is impossible to obtain a 3D model of Pinus massoniana leaves. In this study, an improved algorithm based on density and projection is proposed to estimate the relevant parameters of Pinus massoniana leaves. Finally, five important phenotypic parameters, namely plant height, stem diameter, main stem length, regional leaf length, and total leaf number, are obtained from the skeleton and the point cloud after separation and reconstruction. The experimental results showed that there was a high correlation between the actual value from manual measurement and the predicted value from the algorithm output. The accuracies of the main stem diameter, main stem length, and leaf length were 93.5%, 95.7%, and 83.8%, respectively, which meet the requirements of real applications.

Dataset Information

Geological surface reconstruction from 3D point clouds.

Publications

Geological surface reconstruction from 3D point clouds.

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