A Survey of Shape Similarity Assessment Algorithms for Product Design and Manufacturing Applications

Cardone, Antonio; Gupta, Satyandra K.; Karnik, Mukul

doi:10.1115/1.1577356

Cited by 165 publications

(94 citation statements)

References 48 publications

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“…Funkhouser et al [9] partially matched shape features according to different priorities. More extensive surveys and literature reviews in this area can be found in references [5], [18], and [29].…”

Section: Comparing Shape Models Of Cadmentioning

confidence: 99%

Retrieving Matching CAD Models by Using Partial 3D Point Clouds

Ip¹,

Gupta²

2007

Computer-Aided Design and Applications

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The ability to search for a CAD model that represents a specific physical part is a useful capability that can be used in many different applications. This paper presents an approach to use partial 3D point cloud of an artifact for retrieving the CAD model of the artifact. We assume that the information about the physical parts will be captured by a single 3D scan that produces dense point clouds. CAD models in our approach are represented as polygonal meshes. Our approach involves segmenting the point cloud and CAD mesh models into surface patches. The next step is to identify corresponding surface patches in point clouds and CAD models that could potentially match. Finally, we compute transformations to align the point cloud to the CAD model and compute distance between them. We also present experimental results to show that our approach can be used to retrieve CAD models of mechanical parts. INTRODUCTIONThe ability to search for a CAD model that represents a specific physical part is a useful capability that can be used in many different applications. The following scenario illustrates the usefulness of being able to search for a CAD model based on point cloud generated by a partial scan. Let us assume that a part needs to be replaced in a complex machine. There is no label on the part. Hence the user does not know the part number. The user scans the physical part using a 3D scanner and generates the point cloud. This point cloud is then used by the user to search the CAD database and find the CAD model of this part. The CAD model has the information about the part number and the user is able to order the replacement part using the part number.

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Section: Comparing Shape Models Of Cadmentioning

confidence: 99%

Retrieving Matching CAD Models by Using Partial 3D Point Clouds

Ip¹,

Gupta²

2007

Computer-Aided Design and Applications

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“…Various shape matching methods exist (Tangelder and Veltkamp, 2008;Cardone et al, 2003;Iyer et al, 2005;Li et al, 2010;Brière-Côté et al, 2012;Demirci et al, 2008;Zhu et al, 2012;Ohbuchi et al, 2005) and can be classified in three main categories: feature-based methods, graph-based methods and geometry-based methods (Figure 2). These methods have been implemented and tested and the results are more or less efficient but only when trying to retrieve a shape which is similar to the one of the query from the point of view of form and structure.…”

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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“…In the first substep, the closest neighbors for each FAAV pi ∈ P need to be obtained by using the distance function defined in Equation (2). The distance function accounts for the relevant FAAV attributes.…”

Section: Building the Set Of Theta Intervals For The Faavs Of Set Pmentioning

confidence: 99%

“…We have presented a survey in [2]. Representative techniques include shape histogram-based techniques [9,13,18,21], graph-based techniques [1,10,19], spatial function based techniques [5,6,11,16,17], feature-based techniques [2,3,8,14,22]. The features can be represented as points or vectors in n dimension space where each dimension represents some significant characteristic of the feature.…”

mentioning

confidence: 99%

Similarity Assessment Based on Face Alignment Using Attributed Vectors

Cardone¹,

Gupta²

2006

Computer-Aided Design and Applications

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In certain shape similarity assessment applications, type, locations, and orientations of faces in boundary representations plays a key role in determining similarity between two parts. This paper describes an algorithm that assesses similarity between two parts by explicitly aligning their faces and hence accounts for these face attributes. The approach involves extracting attributed applied vectors from the face information of parts and aligning the two sets of attributed applied vectors using rigid body transformations. The algorithm uses a customizable distance function to align attributed applied vectors. The distance between two aligned attributed applied vector sets is used as a measure of similarity between two parts. This paper also presents computational results to illustrate discrimination capability of the algorithm.Keywords: Shape similarity assessment, alignment of models, and design reuse. INTRODUCTIONOver the last fifteen years 3D CAD systems have become very popular in the industry. These CAD systems are being used to generate 3D models of parts. Nowadays, organizations routinely set up databases of CAD models to enable all participants in the product development process to have access to 3D data to support their functions. CAD databases for even moderate size companies are expected to be large in size. Intuitively, if two products are similar, it is possible to reuse information about one product to derive corresponding information about the other one. There are many possible applications where reuse of information can be of significant value. Representative examples include part-family formation, redesign suggestion generation, supplier selection, cost estimation, tooling design, machine selection, stock selection, and design reuse. The ability to search for similar products in a database by specifying a query product is expected to help companies in significantly reducing the associated time and cost compared with the manual methods of locating the similar products. In [12] an overview on contentbased search techniques for parts and assemblies is given. In certain applications, locations, type, and orientations of faces play a major role in determining similarity between two parts. For example, similarity between two molded parts from the tool maker selection point of view needs to be assessed by analyzing characteristics of part faces. For example, face parameters such as spatial location, type and curvature distribution determine the type and complexity of the tooling needed to manufacture the part. Similarly, the face area determines the size of the tooling. Face features do not always have explicitly defined parameters. Hence we need to identify components of face feature vectors that are significant in determining the similarity between two parts from the tooling point of view. Shape similarity assessment problem has been studied extensively in literature. We have presented a survey in [2]. Representative techniques include shape histogram-based techniques [9,13,18,21], graph-bas...

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A Survey of Shape Similarity Assessment Algorithms for Product Design and Manufacturing Applications

Cited by 165 publications

References 48 publications

Retrieving Matching CAD Models by Using Partial 3D Point Clouds

Retrieving Matching CAD Models by Using Partial 3D Point Clouds

Thin part identification for CAD model classification

Similarity Assessment Based on Face Alignment Using Attributed Vectors

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