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The digitalization of society is rapidly developing toward the realization ofthe digital twin and metaverse. In particular, point clouds are attractingattention as a media format for 3D space. Point cloud data is contaminated withnoise and outliers due to measurement errors. Therefore, denoising and outlierdetection are necessary for point cloud processing. Among them, PointCleanNetis an effective method for point cloud denoising and outlier detection.However, it does not consider the local geometric structure of the patch. Wesolve this problem by applying two types of graph convolutional layer designedbased on the Dynamic Graph CNN. Experimental results show that the proposedmethods outperform the conventional method in AUPR, which indicates outlierdetection accuracy, and Chamfer Distance, which indicates denoising accuracy.

Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN