对于3D点云学习的非参数回归
Xinyi Li1, Shan Yu2, Yueying Wang3
1School of Mathematical and Statistical Sciences, Clemson University Clemson, SC 29634, USA.
概括
本研究介绍了一种新的光滑工具,使用多变量线来从点云创建3D固体模型. 该方法有效地消除数据,重建信号,并减少数据大小,以最佳的收率.
科学领域:
- 计算机科学 计算机科学
- 数学 数学 是一个数学.
- 几何处理的几何处理.
背景情况:
- 具有不规则形状的点云数据的指数增长.
- 固体建模对于从点云中提取信息的重要性.
- 对于稀疏,不规则的数据,需要有效的除和重建方法.
研究的目的:
- 开发用于点云处理的新高效的平滑工具.
- 从点云中提取底层信号并构建3D固体模型.
- 提供理论保证,量化估计不确定性.
主要方法:
- 使用多变量斜线在三角化上进行平滑.
- 应用该方法来消除,消除模糊,并重建点云信号.
- 实施一个引导式方法来量化不确定性.
主要成果:
- 有效地消除点云的模糊和模糊.
- 基础信号和轨迹的多分辨率重建.
- 实现了最佳的非参数收率和高效的数据缩小.
- 在传统的光滑方法上表现出优越性.
结论:
- 拟议的多变量斜线基光滑工具对于从点云进行3D固体建模是有效的.
- 该方法提供了卓越的准确性和数据减少效率.
- 理论上的保证和不确定性量化支持它的可靠性.
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