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Journal Articles SIAM Journal on Imaging Sciences Year : 2015

Robust Geometry Estimation using the Generalized Voronoi Covariance Measure

Abstract

The Voronoi Covariance Measure of a compact set K of R^d is a tensor-valued measure that encodes geometric information on K and which is known to be resilient to Hausdorff noise but sensitive to outliers. In this article, we generalize this notion to any distance-like function delta and define the delta-VCM. We show that the delta-VCM is resilient to Hausdorff noise and to outliers, thus providing a tool to estimate robustly normals from a point cloud approximation. We present experiments showing the robustness of our approach for normal and curvature estimation and sharp feature detection.
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Dates and versions

hal-01058145 , version 1 (26-08-2014)
hal-01058145 , version 2 (04-11-2015)

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Cite

Louis Cuel, Jacques-Olivier Lachaud, Quentin Mérigot, Boris Thibert. Robust Geometry Estimation using the Generalized Voronoi Covariance Measure. SIAM Journal on Imaging Sciences, 2015, 8 (2), pp.1293-1314. ⟨10.1137/140977552⟩. ⟨hal-01058145v2⟩
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