Extracting lines of curvature from noisy point clouds
By Evangelos Kalogerakis, Derek Nowrouzezahrai, Patricio D. Simari, Karan Singh. 2009
100 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- M. Ovsjanikov
- Niloy J. Mitra
- Leonidas J. Guibas
- Paul Guerrero
- Yanir Kleiman
- Quentin Mérigot
- Charles Ruizhongtai Qi
- Vladimir G. Kim
In Computer Graphics and Computer-Aided Design
- M. Ovsjanikov
- Leonidas J. Guibas
- Niloy J. Mitra
- Quentin Mérigot
- Paul Guerrero
- Yanir Kleiman
- Kang Yin
- Simon Fraser
In Computer Vision and Pattern Recognition
- Niloy J. Mitra
- Leonidas J. Guibas
- M. Ovsjanikov
- Luc Van Gool
- Ming-Hsuan Yang
- Paul Guerrero
- Jitendra Malik
- Yanir Kleiman
Citations in the corpus
- PCPNet Learning Local Shape Properties from Raw Point Clouds (2017, background): “In a similar spirit, curvatures can be computed by considering normal variation in a local neighborhood [BC94, KNSS09].”
- A review of algorithms for filtering the 3D point cloud (2017, unknown): “Finally, they maximized a posteriori 𝑃 ( 𝑆 | 𝐷 ) to remove noise while preserving features.”
- Voronoi-Based Curvature and Feature Estimation from Point Clouds (2011, methodology): “[14] use robust statistical methods to fit the curvature tensor of a point cloud by considering normal variation in a neighborhood of a given point.”