基于深度学习的层次感知技术在3D时装设计中的应用
1School of Digital Arts and Design, Dalian Neusoft University of Information, Dalian, 116023, Liaoning, China.
Heliyon
|May 2, 2024
概括
本研究介绍了3D表示中的缺陷检测方法 (FDM-3DR),以增强3D时装设计可视化并减少错误. 该方法通过在过程的早期识别和纠正缺陷,提高了设计适应性和消费者满意度.
科学领域:
- 计算机科学 计算机科学
- 时尚技术 时尚技术 时尚技术
- 人工智能的人工智能
背景情况:
- 3D时装设计增强了可视化和定制性.
- 自动化和智能处理在时装设计中越来越有影响.
- 频繁的设计修改可能是耗时和昂贵的.
研究的目的:
- 在3D表示中引入一个缺陷检测方法 (FDM-3DR).
- 为了减少3D时装设计中的频繁修改.
- 通过增强的设计可视化和更少的缺陷来提高消费者满意度.
主要方法:
- 开发一种可视化3D设计的方法,以确保完整和完美.
- 使用消费者建议来识别设计缺陷.
- 训练一个学习模型,使用可固定表示来排斥不合适的设计.
主要成果:
- FDM-3DR方法根据消费者输入识别并呈现多个设计缺陷.
- 观察到更好的设计适应性,推比率和代表性比率.
- 减少了推时间和设计缺陷的减少.
结论:
- 通过减少缺陷和改善可视化,FDM-3DR方法有效地增强了3D时装设计.
- 这种方法可以提高消费者满意度,提高设计流程的效率.
- 这种方法支持时尚行业的智能处理和自动化.
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