Point-line-surface gradual surface reconstruction emphasizing sharp feature

Zong-chun LI; Hua HE; Yong-jian FU; Guo-jun LI; Wang-min YI

doi:10.3788/OPE.20192701.0221

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Point-line-surface gradual surface reconstruction emphasizing sharp feature

Information Sciences | 更新时间：2020-08-13

- Point-line-surface gradual surface reconstruction emphasizing sharp feature
- Optics and Precision Engineering Vol. 27, Issue 1, Pages: 221-229(2019)
- 作者机构：
  
  1.信息工程大学地理空间信息学院，河南郑州 450001
  2.北京卫星导航中心，北京 100094
  3.北京卫星环境工程研究所，北京 100094
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192701.0221
  CLC： P391.7;P237
- Received：02 April 2018，
  
  Accepted：17 June 2018，
  
  Published：25 January 2019
- 稿件说明：
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Zong-chun LI, Hua HE, Yong-jian FU, et al. Point-line-surface gradual surface reconstruction emphasizing sharp feature[J]. Optics and precision engineering, 2019, 27(1): 221-229.
DOI：

Zong-chun LI, Hua HE, Yong-jian FU, et al. Point-line-surface gradual surface reconstruction emphasizing sharp feature[J]. Optics and precision engineering, 2019, 27(1): 221-229. DOI： 10.3788/OPE.20192701.0221.

摘要

针对现有曲面重建算法不能很好地重建出点云模型尖锐特征的缺陷，提出了一种凸显点云尖锐特征的点-线-面递进式曲面重建算法。首先，根据近邻点的欧氏距离、法向偏差和曲面变分，采用主成分分析算法和

-近邻点迭代加权法获取点云准确法向；接着，依据特征点位于多个平面交线上的原则，利用法向聚类和平面拟合从候选特征点中筛选特征点；然后，依据特征点生长方向和主方向的相互关系重建特征线

并按照最小二乘原理采用矩阵法修复角点；最后，以特征线为约束重建尖锐特征点云曲面。实验结果表明：本文算法计算的点云准确法向与理论法向偏差接近于0，特征重建效果优于其他算法，算法耗时短且与点云数量呈线性关系。算法不仅能够准确计算尖锐特征区域的点云法向，还能准确提取出点云模型的特征点并凸显模型的尖锐特征。

Abstract

To overcome the shortcomings of reconstructing sharp features of the point cloud model in current surface reconstruction algorithms

a gradual point-line-surface reconstruction method emphasizing the sharp features was proposed. First

the accurate vectors of the point cloud were calculated using principal component analysis along with k-neighbors iterative weighting according to spatial distance

normal deviation

and surface variation. Second

based on the principle that feature points were present on the intersection area of several planes

the feature points were screened out by vector clustering and plane fitting from the shortlisted feature points. Next

feature lines were reconstructed according to the mutual relation between growing direction and principal direction of the feature points. Meanwhile

corner points were optimized by matrix method on the basis of the least squares theory. Finally

the surface was reconstructed using the feature lines constraints. Experimental results indicate that the deviation between accurate vectors and theoretical vectors is close to 0. The effect of the proposed algorithm is more superior to the Poisson and MPU algorithms; moreover

the effect is equivalent to the algorithm from reference [

]. Furthermore

there exists a linear relationship between the time and point number

and the algorithm consumes less time. The proposed algorithm can accurately estimate the vectors of the point cloud with sharp features; simultaneously

this algorithm can precisely extract feature points from the point cloud models while emphasizing the sharp features of the models.

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references

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