发表机构
Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出flow-TAG，一种基于生成流模型和1D U-Net的曲线拟合框架，通过映射几何到最优B样条参数化，实现高精度拟合与强泛化，并在ECG压缩中达到13倍压缩比和5%失真。
AI 中文摘要
鲁棒的曲线拟合在计算机辅助设计中至关重要，它将噪声、离散数据转换为精确的几何模型，以确保工程工作流中的数值稳定性。B样条模型已成为该任务的行业标准，提供了灵活可靠的框架，具有局部控制和平滑形状表示的特点。本文提出了flow-TAG——一种基于生成流模型的数据驱动框架，该模型采用一维U-Net骨干网络，能够将曲线的几何形状映射到三次B样条的最优参数化。通过利用学习到的几何模式，flow-TAG展现出优越的参数化性能、对输入数据噪声的鲁棒性，以及对来自不同数据分布的未见过的2D和3D曲线的强泛化能力。与最先进的数据驱动方法相比，flow-TAG生成的拟合曲线实现了更低的均方根误差（平均降低55%）和豪斯多夫距离（平均降低52%）。此外，我们研究了该生成框架在可穿戴设备ECG信号压缩中的实际应用。所提出的压缩方案提供了13的压缩比，信号失真约为5%，这在领域内是可接受的。
英文摘要
Robust curve fitting is essential in computer-aided design for transforming noisy, discrete data into accurate geometric models that ensure numerical stability across engineering workflows. B-spline models have become the industry standard for this task, offering a flexible and reliable framework characterized by local control and smooth shape representation. This paper presents flow-TAG--a data-driven framework based on a generative flow model with a 1D U-Net backbone capable of mapping the geometry of a curve to the optimal parametrization for cubic B-splines. By leveraging learned geometric patterns, flow-TAG exhibits superior parameterization performance, robustness to noise in the input data, and strong generalization capability to previously unseen 2D and 3D curves drawn from distinct data distributions. Flow-TAG yields fitted curves that achieve the lower root-mean-square error (55% lower on average) and Hausdorff distance (52% lower on average) relative to state-of-the-art data-driven methods. In addition, we investigate the practical applicability of our generative framework in the compression of ECG signals for wearable devices. The proposed compression setup provides a compression ratio of 13 with the signal distortion of around 5%, which is acceptable in the field.
