.png){kind=icon}
返回
Robust moving least-squares fitting with sharp features
.png){kind=icon}
PRE
AI
DOI：10.1145/1073204.1073227.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We introduce a robust moving least-squares technique for reconstructing a piecewise smooth surface from a potentially noisy point cloud. We use techniques from robust statistics to guide the creation of the neighborhoods used by the moving least squares (MLS) computation. This leads to a conceptually simple approach that provides a unified framework for not only dealing with noise, but also for enabling the modeling of surfaces with sharp features. Our technique is based on a new robust statistics method for outlier detection: the forward-search paradigm. Using this powerful technique, we locally classify regions of a point-set to multiple outlier-free smooth regions. This classification allows us to project points on a locally smooth region rather than a surface that is smooth everywhere, thus defining a piecewise smooth surface and increasing the numerical stability of the projection operator. Furthermore, by treating the points across the discontinuities as outliers, we are able to define sharp features. One of the nice features of our approach is that it automatically disregards outliers during the surface-fitting phase.
Keyword:
moving least squares
surface reconstruction
robust statistics
forward-search
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.5
论文数:
4.7K
被引数:
3.6W
机构
暂无机构信息
引用论文
Thermoluminescence study of Al2O3 and Y3Al5O12 single crystals irradiated with reactor neutrons
PREAI
分享收藏
分享收藏An inactivated bovine virus diarrhoea virus (BVDV) type 1 vaccine affords clinical protection against BVDV type 2.png)
Vaccine
VaccineIF0
PREAI
分享收藏分享收藏Dynamical Model of Drug Accumulation in Bacteria: Sensitivity Analysis and Experimentally Testable PredictionsPLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容