Shape Dimension and Approximation from Samples

Dey,; Giesen,; Goswami,; Zhao, Lei

doi:10.1007/s00454-002-2838-9

Cited by 37 publications

(45 citation statements)

References 16 publications

(21 reference statements)

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“…The Dimension algorithm presented by Dey et al [23] provides a Voronoi based method to detect dimensions of the shape assigned to a sample point set. [23] also presents CoconeShape algorithm to approximate the shape of the arbitrary dimensions from the sample points.…”

Section: Explicit Methodsmentioning

confidence: 99%

“…[23] also presents CoconeShape algorithm to approximate the shape of the arbitrary dimensions from the sample points. The CoconeShape algorithm acts the same as the Cocone algorithm does, with extra information about the dimensions of the shape achieved by the Dimension algorithm.…”

Section: Explicit Methodsmentioning

confidence: 99%

“…Since the Dimension and CoconeShape algorithms presented in [23] are Voronoi based and the surface approximation algorithm looks alike the Cocone method, Dey et al's approach [23] has the same shortcomings of the Cocone and all Voronoi based surface reconstruction algorithms. These disadvantages include intolerability to noise and sparseness, and performance issue in a large dataset.…”

Section: Triangulated Explicit Methodsmentioning

confidence: 99%

“…These disadvantages include intolerability to noise and sparseness, and performance issue in a large dataset. The results of Dimension algorithm for curve, foot, engine and ball datasets are illustrated in [23]. From the provided results in [23] and [20] it seems that the dimension estimation in [23] increases the total computing time for foot dataset in comparison with [20].…”

Section: Triangulated Explicit Methodsmentioning

confidence: 99%

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Survey on 3D Surface Reconstruction

2016

J Inf Process Syst

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The recent advent of increasingly affordable and powerful 3D scanning devices capable of capturing high resolution range data about real-world objects and environments has fueled research into effective 3D surface reconstruction techniques for rendering the raw point cloud data produced by many of these devices into a form that would make it usable in a variety of application domains. This paper, therefore, provides an overview of the existing literature on surface reconstruction from 3D point clouds. It explains some of the basic surface reconstruction concepts, describes the various factors used to evaluate surface reconstruction methods, highlights some commonly encountered issues in dealing with the raw 3D point cloud data and delineates the tradeoffs between data resolution/accuracy and processing speed. It also categorizes the various techniques for this task and briefly analyzes their empirical evaluation results demarcating their advantages and disadvantages. The paper concludes with a cross-comparison of methods which have been evaluated on the same benchmark data sets along with a discussion of the overall trends reported in the literature. The objective is to provide an overview of the state of the art on surface reconstruction from point cloud data in order to facilitate and inspire further research in this area.

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Section: Explicit Methodsmentioning

confidence: 99%

Section: Explicit Methodsmentioning

confidence: 99%

Section: Triangulated Explicit Methodsmentioning

confidence: 99%

Section: Triangulated Explicit Methodsmentioning

confidence: 99%

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Survey on 3D Surface Reconstruction

2016

J Inf Process Syst

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show abstract

“…Thus, having a highly but irregularly sampled point set can actually work against a localized algorithm. Thus, another stricter sampling condition, known as an (ε, δ)-sampling [28] is required.…”

Section: Epsilon Delta Samplingmentioning

confidence: 99%