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.

翻译：社会数字化正朝着数字孪生与元宇宙的实现快速发展。其中，点云作为一种三维空间媒体格式备受关注。由于测量误差，点云数据会受到噪声和离群点的污染。因此，去噪与离群点检测成为点云处理的必要环节。在现有方法中，PointCleanNet是点云去噪与离群点检测的有效方法，但该方法未考虑局部块（patch）的几何结构。为解决该问题，我们引入基于动态图CNN设计的两种图卷积层。实验结果表明，在衡量离群点检测精度的AUPR指标和衡量去噪精度的Chamfer距离指标上，所提方法均优于传统方法。