超顶点采样网络:3D网格的地质差分SLIC方法
IEEE transactions on visualization and computer graphics
|July 13, 2023
概括
我们介绍了一种用于3D网格细分的新可差异化方法,Geodesic Differential Supervertex (GDSV),可以无地集成到深度学习网络中,以增强3D形状分析和理解.
科学领域:
- 计算机图形 计算机图形
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算几何学的计算几何学
背景情况:
- 对于3D网状分析的深度学习正在增长.
- 层次的网格表示对于多尺度分析至关重要.
- 目前的方法是不可区分的,限制了与可训练网络的集成.
研究的目的:
- 为3D网格提出一种新的可微分图表式细分方法.
- 为了使网格层次结构结构无集成到深度学习框架中.
- 克服现有的不可区分网状加工技术的局限性.
主要方法:
- 拟议的地理差分超顶点 (GDSV),是一种基于图表的可差分段方法.
- 确保了地理位置更新的可区分性,同时保持了多路线上的超顶点.
- 使用差异 SLIC 聚类和 Gumbel-Softmax 技巧进行超顶更新.
主要成果:
- 采用GDSV方法可确保可差分的地理位置更新.
- 该方法将地理位置更新转换为线性矩阵乘法问题.
- 实验结果表明,在各种数据集中表现出色.
结论:
- GDSV为3D网格细分提供了一种可差异化和可整合的方法.
- 该方法可以作为独立模块或深度学习管道中的插件组件.
- GDSV 便于高级 3D 任务,如形状分类,部分细分和场景理解.
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