Adaptive reconstruction of 3D point cloud by sparse optimization

FENG Xiao-wei; HU Hai-yun; ZHUANG Rui-qing; HE Min

doi:10.37188/OPE.20212910.2495

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Adaptive reconstruction of 3D point cloud by sparse optimization

Information Sciences | 更新时间：2026-09-12

- Adaptive reconstruction of 3D point cloud by sparse optimization
- Optics and Precision Engineering Vol. 29, Issue 10, Pages: 2495-2503(2021)
- 作者机构：
  
  上海海事大学电气自动化系，上海 201306
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20212910.2495
  CLC： TP391
- Received：20 April 2021，
  
  Revised：2021-06-29，
  
  Published：15 October 2021
- 稿件说明：
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冯肖维,胡海云,庄睿卿等.三维点云自适应稀疏优化重构[J].光学精密工程,2021,29(10):2495-2503.

FENG Xiao-wei,HU Hai-yun,ZHUANG Rui-qing,et al.Adaptive reconstruction of 3D point cloud by sparse optimization[J].Optics and Precision Engineering,2021,29(10):2495-2503.
冯肖维,胡海云,庄睿卿等.三维点云自适应稀疏优化重构[J].光学精密工程,2021,29(10):2495-2503. DOI： 10.37188/OPE.20212910.2495.

FENG Xiao-wei,HU Hai-yun,ZHUANG Rui-qing,et al.Adaptive reconstruction of 3D point cloud by sparse optimization[J].Optics and Precision Engineering,2021,29(10):2495-2503. DOI： 10.37188/OPE.20212910.2495.

摘要

为了抑制三维点云中包含的噪声，提出一种具有特征保持的稀疏优化重构方法，在抑制噪声的同时恢复尖锐特征。首先，利用邻域点构建的局部张量矩阵的特征值对隐含流形面进行曲率估计，并凭借成对一致性投票实现特征点邻域的鲁棒统计辨识，避免离群点对于法向估计的影响；然后，为了抑制交替优化过程中产生的虚假特征，在L0最小化框架中引入具有特征辨识的自适应微分算子，并依靠投影正则项缓解曲面特征的退化；最后，根据优化后的法向场对尖锐特征进行投影恢复。实验结果表明，经本文所述算法重构后点云的误差平均减小10.2%，法向误差平均减少29.7%，同时主观视觉效果也优于现有多种典型算法。所述方法能够有效提高点云的质量，为基于点云的三维测量与逆向建模提供技术支撑。

Abstract

To suppress 3D point cloud noise

a feature-preserving reconstruction method using sparse optimization is proposed

which can restore sharp features while suppressing noise. First

the curvature of the underlying manifold surface is estimated using the eigenvalues of the local tensor matrix

which is constructed by using the neighboring points. To avoid the influence of outliers on normal estimation

pair consistency voting is used to realize robust statistical identification of feature points in the neighborhood. In the L0 minimization framework

an adaptive differential operator

based on feature identification

is introduced to avoid generation of artifacts in the alternating optimization process

and the projection regularization term is used to alleviate curved surface degradation. According to the optimized normal field

the sharp features are restored by projection optimization. The experimental results show that the reconstructed point cloud error is reduced by 10.2% on average

and the normal error is reduced by 29.7% on average. In addition

the subjective visual effect is better than several state-of-the-art algorithms. The introduced method can effectively improve the point cloud quality and provide technical support for 3D measurement and reverse modeling based on the point cloud.

关键词

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references

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