LGCPNet : Local-global combined point-based network for shape segmentation

Guan, Boliang; Li, Hanhui; Zhou, Fan; Lin, Sheng; Wang, Ruomei

doi:10.1016/j.cag.2021.04.028

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Then, a graph is constructed to encode the geometric and texture information, and a graph convolutional network is used to classify the segments. Guan et al [37] constructed a graph based on the point set extracted from mesh and perform convolution on this graph for features extraction. Besides, local and global features are captured simultaneously and aggregated together for point labeling.…”

Section: Point Cloud Based Methodsmentioning

confidence: 99%

A Texture Integrated Deep Neural Network for Semantic Segmentation of Urban Meshes

Yang

Tang

Xia

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Point Cloud Based Methodsmentioning

confidence: 99%

A Texture Integrated Deep Neural Network for Semantic Segmentation of Urban Meshes

Yang

Tang

Xia

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…An object's mesh structure is first converted into a series of data points with barycenter and normal vector. The data can be segregated and labeled to find characteristics with trained convolutional models [28].…”

Section: State Of the Artmentioning

confidence: 99%

Automated geometric analysis of metallic components through picture recognition models for manufacturing technology assessments

Buechler

Schulz

Zaeh

2022

Prod. Eng. Res. Devel.

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The selection and interaction of various manufacturing technologies are key difficulties in product development and production processes. A component’s geometry is one of the most important factors to consider when choosing the best technology. This article presents a method for an automated geometry analysis of metallic components. The goal is to analyze manufacturing technology alternatives regarding their capability to create required geometries. It also aims at short computing times since the outcome of this geometric analysis supplements a part screening methodology for the selection of the most suitable manufacturing technology for each component. To achieve a successful classification, artificial intelligence (AI) approaches are trained with images of the components that are labeled with suitable manufacturing technologies. The AI models hence learn how components of different manufacturing technologies look like and which characteristics they embody. To support the classification model, object recognition models are tested to automatically extract component features such as holes, coinages, or profile compositions. After training and comparing different AI approaches, the best performers are selected and implemented to analyze unseen image data of upcoming projects. In summary, this article’s research unifies existing AI approaches for image analyses with the field of production technology and product development. It provides a general methodology for applying image classification and object detection approaches in development processes of metallic components.

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