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Start Over Author "Wang Weijia" Publisher elsevier b.v.
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47. Random screening-based feature aggregation for point cloud denoising.

Author

Wang, Weijia, Pan, Wei, Liu, Xiao, Su, Kui, Rolfe, Bernard, and Lu, Xuequan

Subjects
*POINT cloud
Abstract

Raw point clouds captured by sensing devices are often contaminated with noise, which perturbs the fidelity of the original geometric information. Point cloud denoising is therefore an inseparable post-processing step, aiming to remove the noise in the point clouds. Existing point cloud denoising approaches are typically trained on datasets that have uniform point distributions and densities, making them unsuitable for effectively denoising point clouds with severe noise or irregular point distributions. In this paper, we introduce a novel random screening-based feature aggregation method for point cloud denoising. Our key insight is that merging features of dense and sparse points assists with enhancing the quality of point cloud denoising results. In specific, our approach involves randomly screening the features of local point patches and fusing richer geometric information of denser points into sparser point representations. Comprehensive experiments demonstrate that our method achieves state-of-the-art performance in the point cloud denoising task on both synthetic and real-world datasets. [Display omitted] • Random screening-based point feature aggregation is useful for point cloud denoising. • Merging features of dense points into sparser points boosts point cloud denoising. • Our pipeline demonstrates robustness to noise and saves processing time. • Our technique achieves impressive results on severe noise and irregular points. [ABSTRACT FROM AUTHOR]

Published
2023
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