STL File Generation with Data Reduction by the Delaunay Triangulation Method in Reverse Engineering

Hur, Sung-Min; Kim, Ho-Chan; Lee, S.-H.

doi:10.1007/s001700200112

Cited by 22 publications

(8 citation statements)

References 14 publications

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“…Luebke [6] used mesh compression to simplify point cloud. Hur et al [7] used Delaunay triangulation to remove extra point data. Medial meshes is used by Sun et al [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

Liu

Fan

et al. 2019

IEEE Access

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3D point cloud simplification is an important pretreatment in surface reconstruction for sparing computer resources and improving reconstruction speed. However, existing methods often sacrifice the simplification precision to improve the simplification speed, or sacrifice the speed to improve precision. A proper balance between the simplification speed and the simplification accuracy is still a challenge. In this paper, we propose a new simplification method based on the importance of point. Named as detail feature points simplified algorithm (DFPSA), this algorithm has distinct processes to achieve improvements in three aspects. First, a rule of k neighborhood search is set to ensure the points found are the closest to the sample point. In this way, the accuracy of calculated normal vector of the point cloud is significantly improved, and the search speed is largely increased. Second, a formula that considers multiple characteristics for measuring the importance of point is proposed. Thereupon, the main detail features of the point cloud are preserved. Finally, an octree structure is employed to simplify the remaining points, through which holes in reconstructing point cloud are obviously reduced. The DFPSA is applied to four different data sets, and the corresponding results are compared with those of other five algorithms. The experimental results demonstrate that the DFPSA brings better simplification effects than existing counterparts, and the DFPSA not only can simplify point cloud but also has good effect in simplifying subject's narrow contours.INDEX TERMS 3D geometric point cloud simplification, detail feature point, k neighborhood search rule, the importance of point.

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“…Luebke [6] used mesh compression to simplify point cloud. Hur et al [7] used Delaunay triangulation to remove extra point data. Medial meshes is used by Sun et al [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

Liu

Fan

et al. 2019

IEEE Access

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“…Generally, it is important to reduce the amount of data points in the model and convert the data into a usable format for manufacturing and other downstream processes while maintaining accuracy [81]. Much research has been done in this area using different techniques: the Delaunay algorithm [82][83][84], median filtering approach [85], weighting scheme method [86], and the reverse engineering approach [87].…”

Section: Discussionmentioning

confidence: 99%

Recent developments in large-scale dimensional metrology

Peggs

Maropoulos

Hughes

et al. 2009

Proceedings of the Institution of Mechanical Engineers, Part B:

172

117

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With ever-more demanding requirements for the accurate manufacture of large components, dimensional measuring techniques are becoming progressively more sophisticated. This review describes some of the more recently developed techniques and the state-of-the-art in the more well-known large-scale dimensional metrology methods. In some cases, the techniques are described in detail, or, where relevant specialist review papers exist, these are cited as further reading. The traceability of the measurement data collected is discussed with reference to new international standards that are emerging. In some cases, hybrid measurement techniques are finding specialized applications and these are referred to where appropriate.

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“…As an example, Luebke et al [12] used mesh compression to simplify the point cloud. In [13], a group of triangles was selected to be simplified based on the angle between them. The authors then iteratively simplified the selected group by recomputing a Delauney triangulation under specific constraints (e.g., triangle area, angle between neighbouring triangles) and then re-chose the triangles.…”

Section: Introductionmentioning

confidence: 99%

Content-Aware Point Cloud Simplification of Natural Scenes

Arav

Filin

Pfeifer

2022

IEEE Trans. Geosci. Remote Sensing

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Laser scanning technology is becoming ubiquitous in studies involving 3D characterizations of natural scenes, e.g., for geomorphological or archaeological interpretations. Setting the point density in such scanning campaigns is usually dictated by the objects of interest within the site yet is applied to the entire scene. Such campaigns result in large data volumes, which are difficult to analyse and where the objects of interest may be hidden in the redundant data. To reduce these excessive volumes, existing simplification strategies maintain smoothness, preserve discontinuities in the point cloud, but disregard the need to preserve detail at the regions of interest (ROI). To address that, this paper proposes a new, context-aware, subsampling approach that retains the high resolution of objects of interest while reducing the data load of less important regions. To do so, we identify the ROI by means of visual saliency measures and reduce the data volume only at the non-salient regions. To facilitate progressive subsampling the reduction is based on a hierarchical data structure that is surficial in nature. This way, the retained representative points describe the underlying surface rather than an interpolation of it. We demonstrate our proposed model on datasets originating from different scanners that feature a variety of scenes. We compare our results to three common simplification approaches. Our results show a reduced point cloud that is similar to the original and allows analysis of ROI at the required point resolution, regardless of the level of simplification.

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STL File Generation with Data Reduction by the Delaunay Triangulation Method in Reverse Engineering

Cited by 22 publications

References 14 publications

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

Recent developments in large-scale dimensional metrology

Content-Aware Point Cloud Simplification of Natural Scenes

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