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

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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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Acknowledgements

The authors would like to express their gratitude to Rafael Ballester for his help and his valuable inputs.

Funding This work was partially funded by the Swiss National Science Foundation (Grant Number 159225).

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Authors and Affiliations

  1. Department of Informatics, University of Zurich, Binzmühlestrasse 14, 8050, Zurich, Switzerland

    Claudio Mura, Gregory Wyss & Renato Pajarola

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  1. Claudio Mura
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  2. Gregory Wyss
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  3. Renato Pajarola
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Correspondence to Claudio Mura.

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Mura, C., Wyss, G. & Pajarola, R. Robust normal estimation in unstructured 3D point clouds by selective normal space exploration. Vis Comput 34, 961–971 (2018). https://doi.org/10.1007/s00371-018-1542-6

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  • DOI: https://doi.org/10.1007/s00371-018-1542-6

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