Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 7 references that are in the map. First posted on arXiv 11 Oct 2023.
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References · 4
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
- 2019PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Cloudscited by 99
- 2021Towards Efficient Graph Convolutional Networks for Point Cloud Handlingcited by 18
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