DNN-based Parameterization for B-Spline Curve Approximation

• Published in: Computer aided design Vol. 186-187; p. 103897
• Main Authors: Tang, Wenqiang, Yang, Zhouwang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2025
• Subjects: Curve approximation; Deep neural network; Nonlinear optimization; Parameterization; Curve approximation; Deep neural network; Parameterization; Nonlinear optimization
• ISSN: 0010-4485
• DOI: 10.1016/j.cad.2025.103897

B-spline curve parameterization is a complex nonlinear and non-convex optimization problem. Traditional optimization methods often struggle with local minima and are computationally expensive, especially in high-dimensional spaces. We proposes a deep neural network (DNN)-based method to efficiently solve the parameterization problem in B-spline curve approximation. The designed parameterization network (PNet) maps the initial parameterization to an optimized one, transforming the problem into a search for suitable network parameters in a high-dimensional feature space. Due to the over-parameterization nature of DNNs, PNet is robust to initial conditions and less prone to local minima. Furthermore, the smooth regularization and top-K loss function are introduced to further enhance optimization performance. Experimental results show that PNet achieves high-precision approximation with remarkable efficiency, even for large-scale point clouds. •An efficient DNN-based method for B-spline curve parameterization is proposed.•Curve reconstruction is enhanced with smooth regularization and top-K loss.•Our method is robust to initial guesses and efficiently handles large point clouds.