Skip to Main content Skip to Navigation
Journal articles

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.
Complete list of metadatas

Cited literature [11 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01058145
Contributor : Quentin Mérigot <>
Submitted on : Wednesday, November 4, 2015 - 1:33:30 PM
Last modification on : Saturday, March 28, 2020 - 1:15:12 AM
Document(s) archivé(s) le : Friday, February 5, 2016 - 10:25:19 AM

Files

SIIMS-RR.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

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

Share

Metrics

Record views

851

Files downloads

600