A framework for similarity recognition of CAD models

Zehtaban, Leila; Elazhary, Omar; Roller, Dieter

doi:10.1016/j.jcde.2016.04.002

Cited by 38 publications

(13 citation statements)

References 30 publications

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“…The authors recommended that the data classification standard to be firstly used to information structure modeling, and secondly to confirm which information can be acquired via wireless technology in different lifecycle stages. In order to integrate heterogeneous information systems in creating innovative products, the data classification standards of product data and product meta-data were discussed by Zehtaban et al (2016). To achieve sustainable production, Kurilova-Palisaitiene et al (2015) classified the product lifecycle data into six types, which included, product design specifications, manufacturing specifications, service specifications, original product quality assurance, core quality assurance, remanufactured product quality assurance.…”

Section: Classification Of Big Data From the Perspective Of Product Lmentioning

confidence: 99%

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

Ren

Zhang

Liu

et al. 2019

Journal of Cleaner Production

381

189

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Section: Classification Of Big Data From the Perspective Of Product Lmentioning

confidence: 99%

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

Ren

Zhang

Liu

et al. 2019

Journal of Cleaner Production

381

189

View full text Add to dashboard Cite

“…Some of them can also detect partial similarities, i.e. models that are similar only for a subset of their shape [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Content-based CAD assembly model retrieval: Survey and future challenges

Lupinetti

Pernot

Monti

et al. 2019

Computer-Aided Design

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Currently, the content-based retrieval is a problem of major interest in several different fields and, focusing on mechanical engineering, many approaches exist to compare and retrieve single CAD parts, to evaluate shape similarity, to extract features and to segment models. However, most of the proposed approaches do not take into account all the key characteristics of an assembly model, such as the relationships between its components, and the different levels according to which two assembly models can be considered similar, i.e. either globally, partially, or locally. For these reasons, the retrieval of CAD assembly models still faces challenges to fully satisfy designers' expectations. The aim of this paper is to review the state-of-the-art of works addressing the CAD assembly model retrieval and to identify future challenges and possible research directions. Firstly, the paper highlights the user requirements for CAD assembly model retrieval and proposes a set of criteria for analyzing the available methods grouped into the following macro-categories: objective, assembly characterization, assembly descriptor, query specification and type of similarity. Secondly, it describes and characterizes the available methods by organizing them according to the adopted criteria. Finally, it discusses the open issues and future challenges.

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“…As the point cloud consists of a list of 3D coordinates for points, we compare the segmented point cloud with an information object in the BIM or the catalog library of plant 3D CAD system from the viewpoint of similarity of shape and select the information object corresponding to the segmented point cloud. Thus, a 3D shape retrieval method [7,8] is necessary such that, when the segmented point cloud is input as a query, the object that is the most similar shape is retrieved.…”

Section: Introductionmentioning

confidence: 99%

A method of generating depth images for view-based shape retrieval of 3D CAD models from partial point clouds

Kim

Yeo

Cha

et al. 2021

Multimed Tools Appl

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A laser scanner can easily acquire the geometric data of physical environments in the form of a point cloud. Recognizing objects from a point cloud is often required for industrial 3D reconstruction, which should include not only geometry information but also semantic information. However, recognition process is often a bottleneck in 3D reconstruction because it requires expertise on domain knowledge and intensive labor. To address this problem, various methods have been developed to recognize objects by retrieving the corresponding model in the database from an input geometry query. In recent years, the technique of converting geometric data into an image and applying view-based 3D shape retrieval has demonstrated high accuracy. Depth image which encodes depth value as intensity of pixel is frequently used for view-based 3D shape retrieval. However, geometric data collected from objects is often incomplete due to the occlusions and the limit of line of sight. Image generated by occluded point clouds lowers the performance of view-based 3D object retrieval due to loss of information. In this paper, we propose a method of viewpoint and image resolution estimation method for view-based 3D shape retrieval from point cloud query. Automatic selection of viewpoint and image resolution by calculating the data acquisition rate and density from the sampled viewpoints and image resolutions are proposed. The retrieval performance from the images generated by the proposed method is experimented and compared for various dataset.Additionally, view-based 3D shape retrieval performance with deep convolutional neural network has been experimented with the proposed method.

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A framework for similarity recognition of CAD models

Cited by 38 publications

References 30 publications

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

Content-based CAD assembly model retrieval: Survey and future challenges

A method of generating depth images for view-based shape retrieval of 3D CAD models from partial point clouds

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