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Pointfilter: Point Cloud Filtering via Encoder-Decoder Modeling.
- Source :
- IEEE Transactions on Visualization & Computer Graphics; Mar2021, Vol. 27 Issue 3, p2015-2027, 13p
- Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10772626
- Volume :
- 27
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Visualization & Computer Graphics
- Publication Type :
- Academic Journal
- Accession number :
- 148496567
- Full Text :
- https://doi.org/10.1109/TVCG.2020.3027069