Interior Distance Using Barycentric Coordinates
By Raif M. Rustamov, Yaron Lipman, Thomas A. Funkhouser. 2009
53 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
- Charles Ruizhongtai Qi
- Leonidas J. Guibas
- Hao Su
- Li Yi
- Matthias Nießner
- Gianmarco Manzini
- Alessandro Russo
- Andreas Geiger
In Computer Graphics and Computer-Aided Design
- Li Yi
- Leonidas J. Guibas
- Hao Su
- Charles Ruizhongtai Qi
- Natarajan Sukumar
- Keenan Crane
- Gianmarco Manzini
- Alessandro Russo
In Computer Vision and Pattern Recognition
- Leonidas J. Guibas
- Luc Van Gool
- Charles Ruizhongtai Qi
- Jitendra Malik
- Xiaogang Wang
- Andrea Vedaldi
- Dacheng Tao
- Trevor Darrell
Citations in the corpus
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2017, methodology): “We follow [23] to obtain an embedding metric that mimics geodesic distance.”
- Geodesics in heat: A new approach to computing distance based on heat flow (2012, background): “Finally, there is a large literature on smoothed distances [Coifman and Lafon 2006; Fouss et al. 2007; Rustamov et al. 2009; Lipman et al. 2010], which is valuable in contexts where differentiability is required.”
- New perspectives on polygonal and polyhedral finite element methods (2014, background): “The many generalizations, connections, and applications of barycentric coordinates in computer graphics have been extensive [32, 33, 34, 35, 36, 37, 38].”