基于低维流形模型的图拉普拉斯正则化的点云去噪算法
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Point Cloud Denoising Algorithm Based on Graph Laplacian Regularization of a Low Dimensional Manifold Model
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    摘要:

    针对目前点云去噪算法易忽略边缘特征的问题,为了保留点云的显著结构特征,提高点云去噪的精度,提出一种基于低维流形的去噪算法。假设点云分布在高维空间的低维流形上,利用点云间表面的自相似性建立图拉普拉斯模型以近似流形结构,结合图的正则化约束,实现点云的精确去噪,最后通过计算均方误差对算法进行定量评价。实验结果表明,提出的点云去噪算法具有较小的误差,并且能够较好地保留视觉显著结构特征。

    Abstract:

    Aiming at the problem that the current point cloud denoising algorithm is easy to ignore the edge features, in order to retain the significant structural features of the point cloud and improve the accuracy of point cloud denoising, a denoising algorithm based on low-dimensional manifolds is proposed. Assumes that the point cloud is distributed on a low-dimensional manifold in a high-dimensional space, and uses the self-similarity of the surface between the point clouds to establish a Turplacian model to approximate the manifold structure, combined with the regularization constraints of the graph to achieve the accuracy of the point cloud Denoise, and finally evaluate the algorithm quantitatively by calculating the mean square error. Experimental results show that the point cloud denoising algorithm proposed has smaller errors and can better retain the visually significant structural features.

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梁宏.基于低维流形模型的图拉普拉斯正则化的点云去噪算法[J].科技与产业,2021,21(09):37-42

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  • 在线发布日期: 2021-09-19