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点云鲁棒低秩联合估计重构

Point Cloud Reconstruction Based on Robust Low-Rank Collaborative Estimation

  • 摘要: 为了提高三维点云的质量,在抑制噪声的同时恢复尖锐特征,提出一种基于L1稀疏优化的点云鲁棒低秩联合估计重构算法.首先使用鲁棒主成分分析进行点云局部区域低秩建模估计,避免离群点的影响,并根据法向场的变化调整模型,实现点云各向异性自适应降噪;为了提高算法求解效率,利用局部曲率进行尖锐特征辨识,将阈值迭代法与非精确增广拉格朗日乘子法相结合,用于点云不同区域低秩模型的求解;再根据每个优化后局部邻域交叠区域的冗余信息完成点云的全局联合估计重构;最后对尖锐特征点运用投影优化实现边缘特征恢复,解决尖锐特征退化以及边缘毛糙的问题.在公开仿真点云数据与多种典型算法的实验结果表明,所提算法无论是主观视觉效果,还是重构精度与效率均得到改善,与MRPCA算法相比,精度、时效分别提升10.22%和56.52%;在保留点云原有特征信息的同时,可以有效地抑制噪声并恢复尖锐特征,重构效果良好.

     

    Abstract: In order to improve the quality of 3D point cloud, a low-rank collaborative estimation method based on L1 sparse optimization is proposed. Method can both restore sharp features and denoise. Firstly, robust principal component analysis is used for low-rank modeling and estimation to avoid the influence of outliers, and the model is adjusted according to the change of normal field to realize anisotropic adaptive denoising in local area. Secondly, in order to improve the efficiency, the local curvature of each point is estimated to recognize sharp feature. Iterative thresholding method and inexact augmented Lagrange multiplier method are combined to solve the low-rank model in different regions of point cloud. Thirdly, the point positions are determined by collaborative estimation from local result to achieve global reconstruction of 3D point cloud. Finally, the sharp edge features are restored by projection optimization of feature points to solve the problems of sharp feature degradation and irregular edges. Experimental results based on public dataset show that the proposed algorithm has achieved improvements in terms of subjective visual effects, as well as reconstruction accuracy and efficiency. Compared with MRPCA algorithm, the accuracy and efficiency are improved by 10.22% and 56.52% respectively. It can effectively filter noise and restore sharp features while the original feature information of point cloud is retained, and the reconstruction effect is superior.

     

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