Using shape distributions to compare solid models

Ip, Cheuk Yiu; Lapadat, Daniel; Sieger, Leonard; Regli, William C.

doi:10.1145/566282.566322

Cited by 120 publications

(42 citation statements)

References 10 publications

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“…Since the introduction of Osada's shape functions, there were many improvements done and also new shape functions have been implemented, whereas the authors of [51] proposed D2a, an improvement of D2 by considering area ratio of surfaces as additional dimension, whose of [52] split the D2 into three types of distances based on the geometric properties of the line connecting two points (IN, OUT, and MIXED) depending if the line lies completely inside the model or outside or both. They applied this method to compare solid CAD models.…”

Section: Related Workmentioning

confidence: 99%

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3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

Ghorpade

Checchin

Laurent

et al. 2017

EURASIP J. Adv. Signal Process.

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The accelerated advancement in modeling, digitizing, and visualizing techniques for 3D shapes has led to an increasing amount of 3D models creation and usage, thanks to the 3D sensors which are readily available and easy to utilize. As a result, determining the similarity between 3D shapes has become consequential and is a fundamental task in shape-based recognition, retrieval, clustering, and classification. Several decades of research in Content-Based Information Retrieval (CBIR) has resulted in diverse techniques for 2D and 3D shape or object classification/retrieval and many benchmark data sets. In this article, a novel technique for 3D shape representation and object classification has been proposed based on analyses of spatial, geometric distributions of 3D keypoints. These distributions capture the intrinsic geometric structure of 3D objects. The result of the approach is a probability distribution function (PDF) produced from spatial disposition of 3D keypoints, keypoints which are stable on object surface and invariant to pose changes. Each class/instance of an object can be uniquely represented by a PDF. This shape representation is robust yet with a simple idea, easy to implement but fast enough to compute. Both Euclidean and topological space on object's surface are considered to build the PDFs. Topology-based geodesic distances between keypoints exploit the non-planar surface properties of the object. The performance of the novel shape signature is tested with object classification accuracy. The classification efficacy of the new shape analysis method is evaluated on a new dataset acquired with a Time-of-Flight camera, and also, a comparative evaluation on a standard benchmark dataset with state-of-the-art methods is performed. Experimental results demonstrate superior classification performance of the new approach on RGB-D dataset and depth data.

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Section: Related Workmentioning

confidence: 99%

“…The presented approach is completely different from Osada's except the use of PDFs to represent shape. Princeton Shape Benchmark [20,51,54,66] McGill 3D Shape Benchmark [61,63] CAD & VRML models [52,[56][57][58][59] Internet WWW (Polygonal meshes) [21,62] MPEG7 [61] ISDB, CDB, Sculpteur (SCU), SHREC Watertight (SHREC-W) [20,66] …”

Section: Introductionmentioning

confidence: 99%

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

Ghorpade

Checchin

Laurent

et al. 2017

EURASIP J. Adv. Signal Process.

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“…For example, D2 shape distribution is widely used in coarse clustering for its efficiency and robustness. With the complexity of features increase, D2 curve trends to be normal distribution [13]. Since the most commonly used CAD models are of precise topological relationship in the form of B-Rep (boundary representation), the topology is essential for similarity evaluation.…”

Section: Related Work 21 Similarity Assessmentmentioning

confidence: 99%

A similarity-based reuse system for injection mold design in automotive interior industry

Zhou

Liu

et al. 2016

Int J Adv Manuf Technol

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Injection mold design of automotive interior is knowledge-intensive and time-consuming; therefore, it is important to reuse previous design instead of designing from scratch. This work proposed a systematic similarity-based approach to reuse design resources. With transformation of native CAD format to STEP for global and partial retrieval, the process of reuse constitutes five steps: (i) initial retrieval for candidates parts, (ii) precise retrieval for similar case based on typical surface retrieval, (iii) adaptation of similar case such as mold base, (iv) reuse-forming mechanism based on partial retrieval of detailed feature similarity, and (v) instance detection and automatic locating of forming mechanism based on partial similarity. While hierarchical representation of CAD models proposed in our previous work serves as the kernel of retrieval, parametric assembly modeling and visual based automation testing are integrated in the reuse process of mold base in step (iii). A case study of the proposed reuse approach is provided, and feedback from automotive interior industry validated its feasibility.

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“…3g, h). From machining point of view it is not only the problem to recognize different free form shapes, but it goes also [10 ] about cutting forces and feed rates which greatly depends on curvature radius distribution, because of cutting material volume which is cutted in one cutting tool revolution. The other advantage and characteristic of free form surface presentation with Hebbian NN is also the way how compressed point cloud data can be reconstructed again in primary surface, which is very hard to achieve with the use of D2 shape function, or even impossible.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Since the exact computation and mathematical interpretation of free form surface is almost impossible or at least very tiresome, there is a problem of representing free form surface data in such a manner, that all the necessary information needed in later procedures to assess, compare or machine a CAD free form model will be available. Some researchers interpret their models as point clouds [10], others are using second-or first-order statistic parameters [18], others use STL models or maybe feature based model description, etc [1,9]. A common thing to all of these interpretation processes is that they all serve as information carriers, as they collect all the necessary information, on a basis of which some important decisions will be made, also regarding machining technology.…”

Section: Introductionmentioning

confidence: 99%

Technological information extraction of free form surfaces using neural networks

Korošec

2006

Neural Comput & Applic

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The aim of this paper is to show how to predict the accurate machining technology for the particular free form NURBS or B-spline surface. Since that kind of a surface is very hard to describe in an analytical manner, the topological and geometrical information about the surface was acquired with the help of self-organized neural networks (NNs) and firstor second-order statistic parameters. It is proved that the most significant parameter in this process is the curvature, especially when rapid changes of curvature on a free form surface occurred. As the Gaussian distribution of surface curvatures and slope gradient data were presumed, the mean and variance was used for one-dimensional data presentation, and the Hebbian output data vector was used to assess probability, density function and distribution of the presented data. For collecting the maximum amount of surface information, the principal component analysis method inside the Hebbian NN was used.

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Using shape distributions to compare solid models

Cited by 120 publications

References 10 publications

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

A similarity-based reuse system for injection mold design in automotive interior industry

Technological information extraction of free form surfaces using neural networks

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