Scattered Data Interpolation with Multilevel B-Splines
IEEE Transactions on Visualization and Computer Graphics
Loop subdivision surface based progressive interpolation
Journal of Computer Science and Technology
Totally positive bases and progressive iteration approximation
Computers & Mathematics with Applications
Weighted progressive iteration approximation and convergence analysis
Computer Aided Geometric Design
Local progressive-iterative approximation format for blending curves and patches
Computer Aided Geometric Design
Progressive interpolation using loop subdivision surfaces
GMP'08 Proceedings of the 5th international conference on Advances in geometric modeling and processing
Hi-index | 0.00 |
In this paper, we develop the adaptive data fitting algorithms by virtue of the local property of the Progressive-iterative approximation (abbr. PIA), which generates the fitting curve (patch) by adjusting the control points of a blending curve (patch) iteratively. In the adaptive data fitting algorithms, the control points are classified into two classes, namely, active and fixed control points, and only the active control points need to be adjusted in each iteration, thus saving computation greatly. Lots of examples and experimental data are presented to demonstrate the efficiency of the adaptive data fitting algorithm. Since the PIA method can be made parallel easily, the adaptive data fitting algorithm developed in this paper has important applications in parallel large scale data fitting.