Zhao Wenbo, Liu Xianming, Wang Shiqi, Fan Xiaopeng, Zhao Debin
IEEE Trans Vis Comput Graph. 2021 Mar;27(3):1937-1952. doi: 10.1109/TVCG.2019.2944357. Epub 2021 Jan 28.
Distinguishing between geometric features and noise is of paramount importance for mesh denoising. In this paper, a graph-based feature-preserving mesh normal filtering scheme is proposed, which includes two stages: graph-based feature detection and feature-aware guided normal filtering. In the first stage, faces in the input noisy mesh are represented by patches, which are then modelled as weighted graphs. In this way, feature detection can be cast as a graph-cut problem. Subsequently, an iterative normalized cut algorithm is applied on each patch to separate the patch into smooth regions according to the detected features. In the second stage, a feature-aware guidance normal is constructed for each face, and guided normal filtering is applied to achieve robust feature-preserving mesh denoising. The results of experiments on synthetic and real scanned models indicate that the proposed scheme outperforms state-of-the-art mesh denoising works in terms of both objective and subjective evaluations.
区分几何特征和噪声对于网格去噪至关重要。本文提出了一种基于图的特征保留网格法线滤波方案，该方案包括两个阶段：基于图的特征检测和特征感知引导法线滤波。在第一阶段，输入噪声网格中的面由面片表示，然后将其建模为加权图。通过这种方式，特征检测可以转化为一个图割问题。随后，在每个面片上应用迭代归一化割算法，根据检测到的特征将面片分离为平滑区域。在第二阶段，为每个面构建一个特征感知引导法线，并应用引导法线滤波来实现鲁棒的特征保留网格去噪。在合成模型和真实扫描模型上的实验结果表明，所提出的方案在客观和主观评价方面均优于当前最先进的网格去噪方法。
