Variational methods for geometry processing
Date of Issue2014
School of Computer Engineering
Inspired by the success of variational techniques in various applications such as image processing and recent advance in sparse norm minimization, this research explores a class of variational methods with sparse norm for digital geometry processing which has to deal with noisy data and non-smooth features. The variational model mainly contains two terms: one is to keep the intrinsic properties of the function and the other is to make the function satisfy some prior knowledge. Three types of geometry processing problems are studied under the similar variational framework. The research focuses on the specific formulation of the variational model and efficient numerical solvers. Its objective is to develop novel geometry processing algorithms that can effectively and robustly process geometric models characterized by the co-existence of non-smooth features and noise/outliers, which is usually difficult in prior art.
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Computer graphics