UVConv: A UV grid-based convolutional neural network for classification and segmentation of 3D CAD models

ZengHui Su; Jing Bai; Xiangyu Ma; Feiwei Qin

doi:10.1016/j.cagd.2026.102516

February 3, 2026Computer Aided Geometric Design

UVConv: A UV grid-based convolutional neural network for classification and segmentation of 3D CAD models

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Key Points

Classification accuracy shows improvements in processing 3D CAD models, with significant advancements in segmentation techniques.
The UV grid-based approach enhances the performance of convolutional neural networks, achieving higher fidelity in classifying shapes.
Innovative segmentation methods are used to differentiate features within complex 3D models, contributing to better design evaluations.
Leveraging advanced neural networks may enable more efficient workflows in CAD environments, suggesting further optimization in industrial applications.

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Implication

Demonstrates classification and segmentation of 3D CAD models using a UV grid-based convolutional neural network, indicating potential applications in design automation.

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Su et al. (2026) studied this question.

synapsesocial.com/papers/69a7600dc6e9836116a2c74c https://doi.org/10.1016/j.cagd.2026.102516

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UVConv: A UV grid-based convolutional neural network for classification and segmentation of 3D CAD models

Key Points

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Implication

Discussion

Cite This Study

UVConv: A UV grid-based convolutional neural network for classification and segmentation of 3D CAD models

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

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