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Learning Robust Graph-Convolutional Representations for Point Cloud Denoising.
- Source :
- IEEE Journal of Selected Topics in Signal Processing; Feb2021, Vol. 15 Issue 2, p402-414, 13p
- Publication Year :
- 2021
-
Abstract
- Point clouds are an increasingly relevant geometric data type but they are often corrupted by noise and affected by the presence of outliers. We propose a deep learning method that can simultaneously denoise a point cloud and remove outliers in a single model. The core of the proposed method is a graph-convolutional neural network able to efficiently deal with the irregular domain and the permutation invariance problem typical of point clouds. The network is fully-convolutional and can build complex hierarchies of features by dynamically constructing neighborhood graphs from similarity among the high-dimensional feature representations of the points. The proposed approach outperforms state-of-the-art denoising methods showing robust performance in the challenging setup of high noise levels and in presence of structured noise. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19324553
- Volume :
- 15
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Journal of Selected Topics in Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 148970249
- Full Text :
- https://doi.org/10.1109/JSTSP.2020.3047471