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Learning Robust Graph-Convolutional Representations for Point Cloud Denoising
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DOI：10.1109/JSTSP.2020.3047471.png)
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Abstract
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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.
Keywords:
Three-dimensional displays
Noise reduction
Convolution
Feature extraction
Task analysis
Noise measurement
Anomaly detection
Point cloud
denoising
outlier removal
graph neural networks
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