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.
Learning Robust Graph-Convolutional Representations for Point Cloud Denoising / Pistilli, F.; Fracastoro, G.; Valsesia, D.; Magli, E.. - In: IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING. - ISSN 1932-4553. - 15:2(2021), pp. 402-414. [10.1109/JSTSP.2020.3047471]
Learning Robust Graph-Convolutional Representations for Point Cloud Denoising
Pistilli F.;Fracastoro G.;Valsesia D.;Magli E.
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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2879980