Dynamic evaluation of exponential polynomial curves and surfaces via basis transformation
For researchers in computer aided geometric design, this method improves the efficiency and robustness of evaluating exponential polynomial curves and surfaces, though it is an incremental improvement over existing dynamic evaluation algorithms.
The paper presents a robust and efficient algorithm for dynamic evaluation of exponential polynomial curves and surfaces using basis transformation, achieving accurate results for any parameter steps with only arithmetic operations, significantly reducing time costs compared to classical algorithms.
It is shown in "SIAM J. Sci. Comput. 39 (2017):B424-B441" that free-form curves used in computer aided geometric design can usually be represented as the solutions of linear differential systems and points and derivatives on the curves can be evaluated dynamically by solving the differential systems numerically. In this paper we present an even more robust and efficient algorithm for dynamic evaluation of exponential polynomial curves and surfaces. Based on properties that spaces spanned by general exponential polynomials are translation invariant and polynomial spaces are invariant with respect to a linear transform of the parameter, the transformation matrices between bases with or without translated or linearly transformed parameters are explicitly computed. Points on curves or surfaces with equal or changing parameter steps can then be evaluated dynamically from a start point using a pre-computed matrix. Like former dynamic evaluation algorithms, the newly proposed approach needs only arithmetic operations for evaluating exponential polynomial curves and surfaces. Unlike conventional numerical methods that solve a linear differential system, the new method can give robust and accurate evaluation results for any chosen parameter steps. Basis transformation technique also enables dynamic evaluation of polynomial curves with changing parameter steps using a constant matrix, which reduces time costs significantly than computing each point individually by classical algorithms.