Robust normal estimation in unstructured 3D point clouds by selective normal space exploration

Robust normal estimation in unstructured 3D point clouds by selective normal space exploration 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Visual Computer Springer Journals

Robust normal estimation in unstructured 3D point clouds by selective normal space exploration

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision
ISSN
0178-2789
eISSN
1432-2315
D.O.I.
10.1007/s00371-018-1542-6
Publisher site
See Article on Publisher Site

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.

Journal

The Visual ComputerSpringer Journals

Published: May 7, 2018

References

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