Robust Geometry Estimation using the Generalized Voronoi Covariance Measure
Computational Geometry
2015-11-05 v2
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.
Keywords
Cite
@article{arxiv.1408.6210,
title = {Robust Geometry Estimation using the Generalized Voronoi Covariance Measure},
author = {Louis Cuel and Jacques-Olivier Lachaud and Quentin Mérigot and Boris Thibert},
journal= {arXiv preprint arXiv:1408.6210},
year = {2015}
}