Bridging Formal Shape Models and Deep Learning: A Novel Fusion for Understanding 3D Objects Article Swipe
Jincheng Zhang
,
Andrew Willis
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24123874
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24123874
This article describes a novel fusion of a generative formal model for three-dimensional (3D) shapes with deep learning (DL) methods to understand the geometric structure of 3D objects and the relationships between their components, given a collection of unorganized point cloud measurements. Formal 3D shape models are implemented as shape grammar programs written in Procedural Shape Modeling Language (PSML). Users write PSML programs to describe complex objects, and DL networks estimate the configured free parameters of the program to generate 3D shapes. Users write PSML programs to enforce fundamental rules that define an object class and encode object attributes, including shapes, components, size, position, etc., into a parametric representation of objects. This fusion of the generative model with DL offers artificial intelligence (AI) models an opportunity to better understand the geometric organization of objects in terms of their components and their relationships to other objects. This approach allows human-in-the-loop control over DL estimates by specifying lists of candidate objects, the shape variations that each object can exhibit, and the level of detail or, equivalently, dimension of the latent representation of the shape. The results demonstrate the advantages of the proposed method over competing approaches.
Related Topics
Concepts
Computer science
Bridging (networking)
Point cloud
Generative model
Generative grammar
Artificial intelligence
Representation (politics)
Object (grammar)
Parametric statistics
Dimension (graph theory)
Active shape model
Grammar
Mathematics
Law
Statistics
Philosophy
Segmentation
Linguistics
Politics
Political science
Pure mathematics
Computer network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24123874
- https://www.mdpi.com/1424-8220/24/12/3874/pdf?version=1718435344
- OA Status
- gold
- Cited By
- 1
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399745541
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4399745541Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s24123874Digital Object Identifier
- Title
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Bridging Formal Shape Models and Deep Learning: A Novel Fusion for Understanding 3D ObjectsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-15Full publication date if available
- Authors
-
Jincheng Zhang, Andrew WillisList of authors in order
- Landing page
-
https://doi.org/10.3390/s24123874Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/24/12/3874/pdf?version=1718435344Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/24/12/3874/pdf?version=1718435344Direct OA link when available
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Computer science, Bridging (networking), Point cloud, Generative model, Generative grammar, Artificial intelligence, Representation (politics), Object (grammar), Parametric statistics, Dimension (graph theory), Active shape model, Grammar, Mathematics, Law, Statistics, Philosophy, Segmentation, Linguistics, Politics, Political science, Pure mathematics, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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