Point Set Denoising Using Bootstrap-Based Radial Basis Function

This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.

本文研究了径向基函数（radial basis functions）的bootstrap测试误差估计在曲面平滑中的应用，具体聚焦于薄板样条拟合（thin-plate spline fitting）场景。由三维扫描设备生成的点集模型普遍存在带噪数据问题，因此点集去噪是点集建模的核心关注点之一。本文重新探讨了用于求解径向基函数平滑参数的bootstrap测试误差估计方法。本文的主要贡献在于提出了一种基于bootstrap的径向基函数平滑算法。所提方法先执行k近邻搜索（k-nearest neighbour search），再将点集投影至近似薄板样条曲面，由此完成去噪流程的同时可良好保留模型特征。本研究还将所提方法与其他平滑方法开展了对比实验。