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19 May 2022 A cascaded approach for feature-preserving point cloud denoising based on the weighted multi-normal scheme
Ruoyu Li, Guangshuai Liu, Pan Wang, Wenyu Yi, Xurui Li
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Proceedings Volume 12250, International Symposium on Computer Applications and Information Systems (ISCAIS 2022); 122500X (2022) https://doi.org/10.1117/12.2639505
Event: International Symposium on Computer Applications and Information Systems (ISCAIS2022), 2022, Shenzhen, China
Abstract
Point cloud denoising is currently still an attractive topic in computer vision. The main challenge is how to preserve the neat geometric features of 3D models while suppressing surface noise effectively. In this paper, we propose a cascaded approach which sequentially includes normal filtering, feature extraction, multi-normal estimation, and points updating to address this concern. The proposed approach keeps robust and has outstanding feature preserving performance by using the multi-normal strategy and incorporating a new feature-recognized trilateral weight. Experiments on synthetic and real scanned point clouds demonstrate that our approach can preserve various geometric features while suppressing noise reliably and is more competitive than the compared methods.
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Ruoyu Li, Guangshuai Liu, Pan Wang, Wenyu Yi, and Xurui Li "A cascaded approach for feature-preserving point cloud denoising based on the weighted multi-normal scheme", Proc. SPIE 12250, International Symposium on Computer Applications and Information Systems (ISCAIS 2022), 122500X (19 May 2022); https://doi.org/10.1117/12.2639505
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KEYWORDS
Denoising
Feature extraction
Principal component analysis
Visualization
Reconstruction algorithms
Computer aided design
Quantitative analysis
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