Lu Xuequan, Wu Shihao, Chen Honghua, Yeung Sai-Kit, Chen Wenzhi, Zwicker Matthias
IEEE Trans Vis Comput Graph. 2018 Aug;24(8):2315-2326. doi: 10.1109/TVCG.2017.2725948. Epub 2017 Jul 11.
Point set filtering, which aims at reconstructing noise-free point sets from their corresponding noisy inputs, is a fundamental problem in 3D geometry processing. The main challenge of point set filtering is to preserve geometric features of the underlying geometry while at the same time removing the noise. State-of-the-art point set filtering methods still struggle with this issue: some are not designed to recover sharp features, and others cannot well preserve geometric features, especially fine-scale features. In this paper, we propose a novel approach for robust feature-preserving point set filtering, inspired by the Gaussian Mixture Model (GMM). Taking a noisy point set and its filtered normals as input, our method can robustly reconstruct a high-quality point set which is both noise-free and feature-preserving. Various experiments show that our approach can soundly outperform the selected state-of-the-art methods, in terms of both filtering quality and reconstruction accuracy.
点集滤波旨在从相应的噪声输入中重建无噪声的点集，是三维几何处理中的一个基本问题。点集滤波的主要挑战在于在去除噪声的同时保留基础几何的几何特征。当前的点集滤波方法仍在这个问题上存在困难：一些方法并非设计用于恢复尖锐特征，而其他方法则不能很好地保留几何特征，尤其是精细尺度的特征。在本文中，我们受高斯混合模型（GMM）启发，提出了一种用于稳健的特征保留点集滤波的新方法。以噪声点集及其滤波后的法线作为输入，我们的方法能够稳健地重建出一个既无噪声又保留特征的高质量点集。各种实验表明，我们的方法在滤波质量和重建精度方面均能显著优于所选的当前先进方法。
