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Updated: May 21, 2025

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通过正常集成实现轻量级显式3D人类数字化.

Jiaxuan Liu1,2, Jingyi Wu1, Ruiyang Jing1

  • 1Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了一种轻量级的3D人体重建模型,使用扩展卷积和交叉共振注意力. 这种方法可显著减少80%的模型参数,以便在边缘设备上有效部署.

关键词:
一个剥皮的多人线性模型.深度学习是一种深度学习.通常的地图估计估计.三维的人类重建.

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科学领域:

  • 计算机视觉 计算机视觉
  • 3D 计算机图形 3D 计算机图形
  • 机器学习 机器学习

背景情况:

  • 从图像中进行3D人体重建对于各种应用至关重要.
  • 由于高计算需求,大型神经网络在资源受限的边缘设备上部署面临挑战.
  • 优化网络架构是降低计算成本和提高效率的关键.

研究的目的:

  • 提出一个轻量级和高效的3D人类重建模型.
  • 为了平衡重建的准确性与实际应用的计算成本.
  • 为了实现在边缘设备上部署3D人类建模.

主要方法:

  • 扩展卷积和交叉共变注意力机制的整合,用于轻量级的生成网络.
  • 利用一种新的损失函数,设计用于正常地图几何属性,以提高表面重建质量.
  • 开发一个网络架构,捕获多个规模的信息,同时最大限度地降低模型的复杂性.

主要成果:

  • 与现有方法相比,培训参数减少了大约80%.
  • 保持生成3D人类模型的高质量.
  • 通过专门的损失函数,证明了表面重建精度的提高.

结论:

  • 拟议的轻量级模型为3D人类重建提供了有效的解决方案.
  • 集成特定的架构组件和定制的损失函数显著减少了计算要求.
  • 这一进步有助于在边缘设备上实际部署3D人类建模技术.