The digitalization of society is rapidly developing toward the realization of
the digital twin and metaverse. In particular, point clouds are attracting
attention as a media format for 3D space. Point cloud data is contaminated with
noise and outliers due to measurement errors. Therefore, denoising and outlier
detection are necessary for point cloud processing. Among them, PointCleanNet
is an effective method for point cloud denoising and outlier detection.
However, it does not consider the local geometric structure of the patch. We
solve this problem by applying two types of graph convolutional layer designed
based on the Dynamic Graph CNN. Experimental results show that the proposed
methods outperform the conventional method in AUPR, which indicates outlier
detection accuracy, and Chamfer Distance, which indicates denoising accuracy.Comment: 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE 2023