Sketch-based Retrieval and Instantiation of Parametric Parts

Zeng, Long; Dong, Zhi-Kai; Yu, Jiayi Joey; Hong, Jianfeng; Wang, Hongyu

doi:10.1016/j.cad.2019.04.003

Cited by 13 publications

(3 citation statements)

References 28 publications

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“…In [21] and [22], similar parametric shape retrievals are proposed. Reference [21] presents a sketch-based parametric part retrieval method using VD graphs composed of feature vertices and edges.…”

Section: Related Workmentioning

confidence: 99%

“…However, it is not suitable for automatic parametric modeling because the ratio among the dimensions is not considered. In [22], the target and each parametric shape set are expressed by a point and a manifold in a feature space, respectively, using a descriptor space representation; a parametric model is retrieved using the distance between a point and manifolds. This method utilizes a set of classified parametric models for retrieval.…”

Section: Related Workmentioning

confidence: 99%

“…A few similar parametric shape retrieval methods have been reported previously. For example, in [21], sketch-based parametric part retrieval method by view dependent graphs is proposed, and in [22], a parametric shape set nearest to a target is retrieved using descriptor space representation. However, these methods cannot be applied to the proposed method.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Parametric Modeling Technique for Structural Design Standardization

2022

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The challenge in designing new three-dimensional (3D) shapes is that considerable manual effort is required for repeated designing and verification to satisfy the required specifications. Extracting and standardizing parametric design rules from a designed 3D shape dataset will be useful for drastically reducing the design time. This paper describes a framework of automatically generating parametric models, design rules, in order to standardize designs for structural systems. In this framework, a design dataset whose dimensions and shapes have parametric relationships with a target shape is extracted using a new retrieval method. This method is an improvement of a conventional similar shape retrieval method. After the extraction of the design dataset, the corresponding parts between the collected shapes are automatically identified and parametric models are automatically generated. This new similar shape retrieval method has two improvements over the conventional method that uses images of 3D shapes captured from multiple directions. First, 3D shapes are converted to unit shapes by stretching or shrinking along each axis. Second, similar shapes are extracted using a special artificial intelligence (AI) model that learns parametric relationships. The effectiveness of the proposed framework is evaluated using the 3D shape data of various structural shapes. Although the accuracy rate of similar-shape retrieval based on parametric relationships was 62% in the conventional method, by using unit shapes and special AIs, it was improved to 99% in the new method. Moreover, the corresponding parts between similar shapes were automatically identified, and parametric models were automatically generated. The effectiveness of this framework has been demonstrated.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Parametric Modeling Technique for Structural Design Standardization

2022

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ArcMatch: high-performance subgraph matching for labeled graphs by exploiting edge domains

Bonnici,

Grasso,

Micale

et al. 2024

Data Min Knowl Disc

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Consider a large labeled graph (network), denoted the target. Subgraph matching is the problem of finding all instances of a small subgraph, denoted the query, in the target graph. Unlike the majority of existing methods that are restricted to graphs with labels solely on vertices, our proposed approach, named can effectively handle graphs with labels on both vertices and edges. ntroduces an efficient new vertex/edge domain data structure filtering procedure to speed up subgraph queries. The procedure, called path-based reduction, filters initial domains by scanning them for paths up to a specified length that appear in the query graph. Additionally, ncorporates existing techniques like variable ordering and parent selection, as well as adapting the core search process, to take advantage of the information within edge domains. Experiments in real scenarios such as protein–protein interaction graphs, co-authorship networks, and email networks, show that s faster than state-of-the-art systems varying the number of distinct vertex labels over the whole target graph and query sizes.

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