Obtaining a best fitting plane through 3D georeferenced data

Fernández, Òscar

doi:10.1016/j.jsg.2004.12.004

Cited by 131 publications

(103 citation statements)

References 2 publications

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“…Several studies are proposing different ways to characterize discontinuity sets, which can be mainly classified as (a) using fitting planes (Fernández 2005;Abellan et al 2006;Oppikofer et al 2008;Sturzenegger and Stead 2009), (b) using TIN surfaces as indicators of the plane orientations (Feng et al 2001;Slob et al 2002). A description of the methodology employed for rockmass characterization using the commercial software Split-FX is provided in Kemeny and Post (2003) and Kemeny and Turner (2008).…”

Section: Tlsmentioning

confidence: 99%

Use of LIDAR in landslide investigations: a review

et al. 2010

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This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and susceptibility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDARderived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be developed.

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Section: Tlsmentioning

confidence: 99%

Use of LIDAR in landslide investigations: a review

et al. 2010

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“…For both methods, FACETS implements a least square fitting algorithm (e.g. Fernandez, 2005). Once the space has been recursively divided, elementary subdivision are back clustered together according to a co-planarity criterion.…”

Section: General Approachmentioning

confidence: 99%

Facets : A Cloudcompare Plugin to Extract Geological Planes From Unstructured 3d Point Clouds

Dewez¹,

Girardeau-Montaut²,

Allanic³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Geological planar facets (stratification, fault, joint…) are key features to unravel the tectonic history of rock outcrop or appreciate the stability of a hazardous rock cliff. Measuring their spatial attitude (dip and strike) is generally performed by hand with a compass/clinometer, which is time consuming, requires some degree of censoring (i.e. refusing to measure some features judged unimportant at the time), is not always possible for fractures higher up on the outcrop and is somewhat hazardous. 3D virtual geological outcrop hold the potential to alleviate these issues. Efficiently segmenting massive 3D point clouds into individual planar facets, inside a convenient software environment was lacking. FACETS is a dedicated plugin within CloudCompare v2.6.2 (http://cloudcompare.org/) implemented to perform planar facet extraction, calculate their dip and dip direction (i.e. azimuth of steepest decent) and report the extracted data in interactive stereograms. Two algorithms perform the segmentation: Kd-Tree and Fast Marching. Both divide the point cloud into sub-cells, then compute elementary planar objects and aggregate them progressively according to a planeity threshold into polygons. The boundaries of the polygons are adjusted around segmented points with a tension parameter, and the facet polygons can be exported as 3D polygon shapefiles towards third party GIS software or simply as ASCII comma separated files. One of the great features of FACETS is the capability to explore planar objects but also 3D points with normals with the stereogram tool. Poles can be readily displayed, queried and manually segmented interactively. The plugin blends seamlessly into CloudCompare to leverage all its other 3D point cloud manipulation features. A demonstration of the tool is presented to illustrate these different features. While designed for geological applications, FACETS could be more widely applied to any planar objects.For further details: http://www.cloudcompare.org/doc/wiki/index.php?title=Facets_%28plugin%29

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“…Early studies on the identification of discontinuity surfaces apply a least squares method to subsets of points in order to determine the best-fit plane through planar regression (Abellán et al, 2006;Fernández, 2005). In the past years, the calculation of normal vectors in association with point cloud subsets has become a widely accepted methodology (Riquelme et al, 2014).…”

Section: State Of the Artmentioning

confidence: 99%

3D GEOLOGICAL OUTCROP CHARACTERIZATION: AUTOMATIC DETECTION OF 3D PLANES (AZIMUTH AND DIP) USING LiDAR POINT CLOUDS

Anders¹,

Hämmerle²,

Miernik³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Terrestrial laser scanning constitutes a powerful method in spatial information data acquisition and allows for geological outcrops to be captured with high resolution and accuracy. A crucial aspect for numerous geologic applications is the extraction of rock surface orientations from the data. This paper focuses on the detection of planes in rock surface data by applying a segmentation algorithm directly to a 3D point cloud. Its performance is assessed considering (1) reduced spatial resolution of data and (2) smoothing in the course of data pre-processing. The methodology is tested on simulations of progressively reduced spatial resolution defined by varying point cloud density. Smoothing of the point cloud data is implemented by modifying the neighborhood criteria during normals estimation. The considerable alteration of resulting planes emphasizes the influence of smoothing on the plane detection prior to the actual segmentation. Therefore, the parameter needs to be set in accordance with individual purposes and respective scales of studies. Furthermore, it is concluded that the quality of segmentation results does not decline even when the data volume is significantly reduced down to 10%. The azimuth and dip values of individual segments are determined for planes fit to the points belonging to one segment. Based on these results, azimuth and dip as well as strike character of the surface planes in the outcrop are assessed. Thereby, this paper contributes to a fully automatic and straightforward workflow for a comprehensive geometric description of outcrops in 3D.

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Obtaining a best fitting plane through 3D georeferenced data

Cited by 131 publications

References 2 publications

Use of LIDAR in landslide investigations: a review

Use of LIDAR in landslide investigations: a review

Facets : A Cloudcompare Plugin to Extract Geological Planes From Unstructured 3d Point Clouds

3D GEOLOGICAL OUTCROP CHARACTERIZATION: AUTOMATIC DETECTION OF 3D PLANES (AZIMUTH AND DIP) USING LiDAR POINT CLOUDS

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