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• W3089927720 endingPage "2027" @default.
• W3089927720 startingPage "2015" @default.
• W3089927720 abstract "Point cloud filtering is a fundamental problem in geometry modeling and processing. Despite of significant advancement in recent years, the existing methods still suffer from two issues: 1) they are either designed without preserving sharp features or less robust in feature preservation; and 2) they usually have many parameters and require tedious parameter tuning. In this article, we propose a novel deep learning approach that automatically and robustly filters point clouds by removing noise and preserving their sharp features. Our point-wise learning architecture consists of an encoder and a decoder. The encoder directly takes points (a point and its neighbors) as input, and learns a latent representation vector which goes through the decoder to relate the ground-truth position with a displacement vector. The trained neural network can automatically generate a set of clean points from a noisy input. Extensive experiments show that our approach outperforms the state-of-the-art deep learning techniques in terms of both visual quality and quantitative error metrics. The source code and dataset can be found at https://github.com/dongbo-BUAA-VR/Pointfilter." @default.
• W3089927720 created "2020-10-08" @default.
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• W3089927720 date "2021-03-01" @default.
• W3089927720 modified "2023-10-14" @default.
• W3089927720 title "Pointfilter: Point Cloud Filtering via Encoder-Decoder Modeling" @default.
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• W3089927720 doi "https://doi.org/10.1109/tvcg.2020.3027069" @default.
• W3089927720 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32986553" @default.
• W3089927720 hasPublicationYear "2021" @default.
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