Voronoi-based Variational Reconstruction of Unoriented

By Ayushi Sinha. 2013

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Carlos Hernández
2. Paul Guerrero
3. Alexandre Boulch
4. Niloy J. Mitra
5. Yanir Kleiman
6. Yasutaka Furukawa
7. Renaud Marlet
8. Andreas Geiger

Citations in the corpus

• PCPNet Learning Local Shape Properties from Raw Point Clouds (2017, methodology): “…osculating jets [CP03] based on fitting higher-order primitives robustly, the algebraic point set surface approach based on fitting algebraic spheres [GG07], and approaches based on analyzing the distribution of Voronoi cells in the neighborhood of a point [AB99, ACSTD07, MOG11], among many others.”
• Deep Learning for Robust Normal Estimation in Unstructured Point Clouds (2016, methodology): “To address it, Alliez et al. [ACSTD07] treat cell distortion due to noise using a PCA-Voronoï approach to create elongated cell sets grouping adjacent Voronoï cells.”
• Multi-View Stereo: A Tutorial (2015, methodology): “There also exist methods to reconstruct a mesh from unoriented point cloud, and hence, unsigned distance functions [103, 29].”