Reverse Engineering Physical Models Employing Wrap-Around B-Spline Surfaces and Quadrics

Weir, D. J.; Milroy, M. J.; Bradley, Colin; Vickers, G. W.

doi:10.1243/pime_proc_1996_210_100_02

Cited by 43 publications

(11 citation statements)

References 10 publications

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“…Two kinds of simplification methods used are: uniform method [1,17] and surface variance method [15,16,18]. Uniform method samples the data space by a fixed size cube.…”

Section: B Data Simplificationmentioning

confidence: 99%

A Method for Surface Reconstruction Based on Support Vector Machine

Zhang¹,

Wang²,

Li³

et al. 2009

JCP

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Surface reconstruction is one of the main parts of reverse engineering and environment modeling. In this paper a method for reconstruct surface based on Support Vector Machine (SVM) is proposed. In order to overcome the inefficiency of SVM, a feature-preserved nonuniform simplification method is employed to simplify cloud points set. The points set is reduced while the feature is preserved after simplification. Then a reconstruction method based on segmented data is proposed to accelerate SVM regression process for cloud data. Firstly, the original sampling data set is partitioned to generate several training data subsets and testing data subsets. A segmentation technique is adopted to keep the continuity on the borders. Secondly regression calculation is executed on every training subset to generate a SVM model, from which a segmented mesh is obtained according to the testing data subset. Finally, all the mesh surfaces are stitched into one whole surface. Both theoretical analysis and experimental result show that the segmentation technique presented in this paper is efficient to improve the performance of the SVM regression, while keeping the continuity of the subset borders.

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“…Two kinds of simplification methods used are: uniform method [1,17] and surface variance method [15,16,18]. Uniform method samples the data space by a fixed size cube.…”

Section: B Data Simplificationmentioning

confidence: 99%

A Method for Surface Reconstruction Based on Support Vector Machine

Zhang¹,

Wang²,

Li³

et al. 2009

JCP

View full text Add to dashboard Cite

show abstract

“…Three kinds of simplification methods used are: uniform method [1,14] and surface variance method [15][16][17]. Uniform method samples the data space by a fixed size cube.…”

Section: B Data Simplificationmentioning

confidence: 99%

Surface reconstruction from cloud points based on Support Vector Machine

Zhang

Liu

et al. 2008

2008 IEEE International Conference on Automation and Logistics

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Surface reconstruction based on Support Vector Machine (SVM)is a hot topic in the field of 3D surface construction. SVM based method for surface reconstruction can reduce the noise in the sampled data as well as repair the holes. However, the regress speed of SVM is too slow to reconstruct surface quickly from cloud points data set which has a lot of points. In this paper, a feature-preserved nonuniform simplification method for cloud points is presented, using which to simplifying points set. This method simplifies the data set to remove the redundancy while keeping down the features of the model. Then the surface is reconstructed from the simplified data using SVM. Both theoretical analysis and experimental results show that after the simplification, the performance of method for surface reconstruction based on SVM is improved greatly as well as the details of the surface are preserved well.I.

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“…Lounsbery et al 20 simplified the connected triangular mesh through wavelet representation. Weir et al 21 proposed a simplification algorithm based on the bounding box. The algorithm constructed a bounding box that surrounded all the points of the 3D model, and the bounding box was divided into several small cubes evenly, and the closest point from the center of the small cube replaced all other points.…”

Section: Introductionmentioning

confidence: 99%

Feature-preserving simplification framework for 3D point cloud

et al. 2022

Sci Rep

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To obtain a higher simplification rate while retaining geometric features, a simplification framework for the point cloud is proposed. Firstly, multi-angle images of the original point cloud are obtained with a virtual camera. Then, feature lines of each image are extracted by deep neural network. Furthermore, according to the proposed mapping relationship between the acquired 2D feature lines and original point cloud, feature points of the point cloud are extracted automatically. Finally, the simplified point cloud is obtained by fusing feature points and simplified non-feature points. The proposed simplification method is applied to four data sets and compared with the other six algorithms. The experimental results demonstrate that our proposed simplification method has the superiority in terms of both retaining geometric features and high simplification rate.

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Reverse Engineering Physical Models Employing Wrap-Around B-Spline Surfaces and Quadrics

Cited by 43 publications

References 10 publications

A Method for Surface Reconstruction Based on Support Vector Machine

A Method for Surface Reconstruction Based on Support Vector Machine

Surface reconstruction from cloud points based on Support Vector Machine

Feature-preserving simplification framework for 3D point cloud

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