学习自我先验用于网格绘画使用自主监督图形卷积网络
IEEE transactions on visualization and computer graphics
|February 9, 2024
概括
本研究引入了一种使用图形卷积网络 (GCNs) 的新型网格绘制框架,可以在没有训练数据的情况下填补3D模型中的漏洞. 该方法保留了网格格式,为不完整的形状提供了强大的性能.
科学领域:
- 计算机图形 计算机图形
- 几何建模 几何建模
- 人工智能的人工智能
背景情况:
- 3D网格完成对于各种应用至关重要.
- 现有的方法通常需要大型数据集或将网格转换为中间格式.
- 需要独立于数据集的方法来实现稳定性和处理新型形状.
研究的目的:
- 为了呈现一个基于自我先验的网格绘制框架.
- 避免依赖训练数据集和中间形状表示.
- 在整个涂漆过程中保持多边形网格格式.
主要方法:
- 引入了单一分辨率GCN (SGCN) 和多分辨率GCN (MGCN).
- 采用自主监督的培训策略,使用"假洞".
- 通过GCNs预测的顶点位移,精制了一个初始的密水网.
主要成果:
- 该框架成功地在没有培训数据的情况下绘制了不完整的网格.
- 保持了多边形网格格式,避免了voxel网格或点云.
- 超越了传统的数据集独立方法,并显示了对罕见形状的深度学习替代品的稳定性.
结论:
- 拟议的基于GCN的网状涂料框架提供了一个无需数据集的强大解决方案.
- 保持网格格式简化了这个过程,并提高了应用性.
- 使用假洞的自我监督方法可以有效地学习顶点位移,以准确完成.
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