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Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3D visualization model construction based on generative adversarial networks.

Xiaojuan Liu¹, Shangbo Zhou², Sheng Wu³

¹College of Computer Science, Chongqing University, Chongqing, China.

Peerj. Computer Science

|May 2, 2022

Summary

This summary is machine-generated.

This study introduces Multi-angle projective Generative Adversarial Networks (MapGANs) for automated manufacturing quality control. MapGANs generate 3D models from 2D images, enabling accurate product quality assessment.

Keywords:

3D visualization model Generation adversarial network Neural network Precision components

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Area of Science:

Computer Vision
Artificial Intelligence
Manufacturing Engineering

Background:

Rapid advancements in computer vision technology enable efficient and reliable automated quality control for precision components.
Traditional quality control methods can be time-consuming and prone to human error, necessitating innovative solutions.

Purpose of the Study:

To present a novel deep learning algorithm, Multi-angle projective Generative Adversarial Networks (MapGANs), for automated quality control in manufacturing.
To develop a method for generating accurate 3D visualization models of products and components from 2D images.
To enable precise determination of product quality based on visualized parameters.

Main Methods:

Development and application of Multi-angle projective Generative Adversarial Networks (MapGANs).
MapGANs infer 3D shape distribution from product projections using a projection module.
Utilizing multiple angles and views to enhance the accuracy and detail of 3D visualization models.

Main Results:

MapGANs effectively reconstruct 2D images into detailed 3D visualization models.
The generated 3D models accurately display product parameters and indicators.
The model accurately predicts whether products meet quality standards based on these indicators.

Conclusions:

MapGANs offer a robust solution for automated quality control in precision component manufacturing.
The algorithm successfully bridges the gap between 2D imaging and 3D product visualization for quality assessment.
This approach enhances the efficiency, reliability, and accuracy of manufacturing quality control processes.