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Robust Normal Estimation in Unstructured 3D Point Clouds by Selective Normal Space Exploration


Mura, Claudio; Wyss, Gregory; Pajarola, R (2018). Robust Normal Estimation in Unstructured 3D Point Clouds by Selective Normal Space Exploration. Visual Computer, 34(6-8):961-971.

Abstract

We present a fast and practical approach for estimating robust normal vectors in unorganized point clouds. Our proposed technique is robust to noise and outliers and can preserve sharp features in the input model while being significantly faster than the current state-of-the-art alternatives. The key idea to this is a novel strategy for the exploration of the normal space: First, an initial candidate normal vector, optimal under a robust least median norm, is selected from a discrete subregion of this space, chosen conservatively to include the correct normal; then, the final robust normal is computed, using a simple, robust procedure that iteratively refines the candidate normal initially selected. This strategy allows us to reduce the computation time significantly with respect to other methods based on sampling consensus and yet produces very reliable normals even in the presence of noise and outliers as well as along sharp features. The validity of our approach is confirmed by an extensive testing on both synthetic and real-world data and by a comparison against the most relevant state-of-the-art approaches.

Abstract

We present a fast and practical approach for estimating robust normal vectors in unorganized point clouds. Our proposed technique is robust to noise and outliers and can preserve sharp features in the input model while being significantly faster than the current state-of-the-art alternatives. The key idea to this is a novel strategy for the exploration of the normal space: First, an initial candidate normal vector, optimal under a robust least median norm, is selected from a discrete subregion of this space, chosen conservatively to include the correct normal; then, the final robust normal is computed, using a simple, robust procedure that iteratively refines the candidate normal initially selected. This strategy allows us to reduce the computation time significantly with respect to other methods based on sampling consensus and yet produces very reliable normals even in the presence of noise and outliers as well as along sharp features. The validity of our approach is confirmed by an extensive testing on both synthetic and real-world data and by a comparison against the most relevant state-of-the-art approaches.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Software
Physical Sciences > Computer Vision and Pattern Recognition
Physical Sciences > Computer Graphics and Computer-Aided Design
Uncontrolled Keywords:graphics, point cloud, normal estimation, robust statistics, surface reconstruction
Language:English
Date:June 2018
Deposited On:20 Feb 2019 15:47
Last Modified:18 Jun 2021 07:26
Publisher:Springer
ISSN:0178-2789
OA Status:Green
Publisher DOI:https://doi.org/10.1007/s00371-018-1542-6
Other Identification Number:merlin-id:17266

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