论文速看[2019-1-7]-Multi-scale tensor voting for feature extraction from unstructured point clouds

一篇关于从无结构化点云特征提取的文章
2012 Graphical Models
摘要

Identifying sharp features in a 3D model is essential for shape analysis, matching and a wide range of geometry processing applications. This paper presents a new method based on the tensor voting theory to extract sharp features from an unstructured point cloud which may contain random noise, outliers and artifacts. Our method first takes the voting tensors at every point using the corresponding neighborhoods and computes the feature weight to infer the local structure via eigenvalue analysis of the tensor. The optimal scale for a point is automatically determined by observing the feature weight variation in order to deal with both a noisy smooth region and a sharp edge. We finally extract the points at sharp features using adaptive thresholding of the feature weight and the feature completion process. The multi-scale tensor voting of a given point set improves noise sensitivity and scale dependency of an input model. We demonstrate the strength of the proposed method in terms of efficiency and robustness by comparing it with other feature detection algorithms.

作者总结了4种从无结构化点云提取特征的方法：

1. PCA
2. graph theory图形学理论
3. surface reconstruction
4. Voronoi-based method
   但是这些方法对输入点云的质量要求较高、或者计算复杂度高。

main contribution

作者提出的一种张量投票算法

1. take the voting tensors at every point by integrating the tensors of the neighbouring points.
2. compute the feature weight using eigenvalue
3. extend the voting theory to multi-scale representation
4. an optimal scale selection method to determine the scale for a point