Using shape distributions to compare solid models

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

doi:10.1145/566313.566322

Cited by 36 publications

(29 citation statements)

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“…They used properties such as the moment of inertia, average distance from surface to the principal axis and distance variation to the principal axis for creating histograms. Ip et al (2002) applied the shape distribution approach for the CAD models by reforming Osada's D2 function. This method was only useful for volume models and not for soup models.…”

Section: Related Workmentioning

confidence: 99%

A K-Means Clustering Based Shape Retrieval Technique for 3d Mesh Models

Rezaei

Günpinar

2018

Scitech

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Due to the large size of shape databases, importance of effective and robust method in shape retrieval has been increased. Researchers mainly focus on finding descriptors which is suitable for rigid models. Retrieval of non-rigid models is a still challenging field which needs to be studied more. For non-rigid models, descriptors that are designed should be insensitive to different poses. For nonrigid model retrieval, we propose a new method which first divides a model into clusters using geodesic distance metric and then computes its descriptor using the area of these clusters. A skeleton-based Kmeans clustering method is utilized for dividing the model into clusters. Each cluster is represented by an area based descriptor which is invariant to scale and orientation. Articulated objects from human to animals are employed in this study's experiments for the validation of the proposed retrieval algorithm.

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Section: Related Workmentioning

confidence: 99%

A K-Means Clustering Based Shape Retrieval Technique for 3d Mesh Models

Rezaei

Günpinar

2018

Scitech

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“…[8] Osada and the shape of the model, it is proved that the Euclidean distance of sh ape distribution is the most effective method to measure the properties of random surface points.At t he same time, the application of et.al. [9] Ip to the shape distribution in the CAD and solid modeling, the Euclidean distance is redefined,moreover, it is very useful for the entity model. Therefore, the E uclidean distance is used to measure the pipe segment model.…”

Section: Precast Pipe Sectionmentioning

confidence: 99%

The Application of 3D Model Retrieval Technique in Pipe Segment Merging Number

Zhao¹,

Liu²

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Abstract. This paper introduces the concept and basic framework of 3D model retrieval technology based on shape distribution, At the same time, according to the shape feature extraction and processing method of the model, the retrieval technique is introduced and applied in detail. This paper focuses on how to apply the 3D model retrieval technology based on shape distribution to the CAD/CAM, especially in the pipe segment merging number of the actual project. Today, with the rapid development of computer aided design, the application of this technology can meet the needs of the factory design, and it is also a breakthrough in the technical level. The application of 3D model retrieval technology based on shape distribution is a breakthrough attempt, although it has some technical problems, but it is of great significance.

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“…Among the existing methods, the one proposed by Osada et al (2002) introduces the so-called "D2 Shape Distribution" to represent, in a normalized histogram, the probability of occurrence of Euclidean distances between pairs of randomly chosen points on the skin of the object. The application of shape distributions in the CAD context has been investigated by Cheng et al (2011) and by Ip et al (2002). Ip et al refined Osada's D2 shape distribution function by classifying two random points distances according to whether the joining line segment connecting the points lies both inside and outside the model.…”

Section: Related Workmentioning

confidence: 99%

Thin part identification for CAD model classification

Pernot

Giannini

Petton

2015

Engineering Computations

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Purpose -The purpose of this paper is to focus on the characterization and classification of parts with respect to the meshing issue, and notably the meshing of thin parts difficulty handled automatically and which often requires adaptation steps. The objective is to distinguish the so-called thin parts and parts with thin features from the other parts. Design/methodology/approach -The concepts of thin part and part with thin features are introduced together with the mechanisms and criteria used for their identification in a CAD models database. The criteria are built on top of a set of shape descriptors and notably the distance distribution which is used to characterize the thickness of the object. To speed up the identification process, shape descriptors are computed from tessellated parts. Findings -A complete modular approach has been designed. It computes shape descriptors over parts stored in a directory and it uses criteria to distinguish three categories: thin parts, parts with thin features and other parts. Being the three categories identified, the user can spend more time on the parts that are considered as more difficulty meshable.Research limitations/implications -The approach is limited to the three above mentioned categories. However, it has been designed so that the values corresponding to the shape descriptors and associated meshing qualities can easily be inserted within a machining learning tool later on. Practical implications -The use of the developed tool can be seen as a pre-processing step during the preparation of finite element (FE) simulation models. It is automatic and can be run in batch and in parallel. Originality/value -The approach is modular, it is simple and easy to implement. Categories are built on top of several shape descriptors and not on a unique signature. It is independent of the CAD modeler. This approach is integrated within a FE simulation model preparation framework and help engineers anticipating difficulties when meshing CAD models.

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Using shape distributions to compare solid models

Cited by 36 publications

References 0 publications

A K-Means Clustering Based Shape Retrieval Technique for 3d Mesh Models

A K-Means Clustering Based Shape Retrieval Technique for 3d Mesh Models

The Application of 3D Model Retrieval Technique in Pipe Segment Merging Number

Thin part identification for CAD model classification

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