Guo Mingqiang, Song Zhenzhen, Han Chengde, Zhong Saishang, Lv Ruina, Liu Zheng
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China.
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, China.
Sensors (Basel). 2021 Jan 8;21(2):412. doi: 10.3390/s21020412.
In this paper, we propose a novel guided normal filtering followed by vertex updating for mesh denoising. We introduce a two-stage scheme to construct adaptive consistent neighborhoods for guided normal filtering. In the first stage, we newly design a consistency measurement to select a coarse consistent neighborhood for each face in a patch-shift manner. In this step, the selected consistent neighborhoods may still contain some features. Then, a graph-cut based scheme is iteratively performed for constructing different adaptive neighborhoods to match the corresponding local shapes of the mesh. The constructed local neighborhoods in this step, known as the adaptive consistent neighborhoods, can avoid containing any geometric features. By using the constructed adaptive consistent neighborhoods, we compute a more accurate guide normal field to match the underlying surface, which will improve the results of the guide normal filtering. With the help of the adaptive consistent neighborhoods, our guided normal filtering can preserve geometric features well, and is robust against complex shapes of surfaces. Intensive experiments on various meshes show the superiority of our method visually and quantitatively.
在本文中，我们提出了一种新颖的引导法线滤波，随后进行顶点更新以实现网格去噪。我们引入了一种两阶段方案来为引导法线滤波构建自适应一致邻域。在第一阶段，我们新设计了一种一致性度量，以面片移位的方式为每个面选择一个粗略的一致邻域。在这一步中，所选的一致邻域可能仍包含一些特征。然后，基于图割的方案被迭代执行以构建不同的自适应邻域，以匹配网格的相应局部形状。在这一步中构建的局部邻域，即自适应一致邻域，可以避免包含任何几何特征。通过使用构建的自适应一致邻域，我们计算出更准确的引导法线场以匹配基础表面，这将改善引导法线滤波的结果。借助自适应一致邻域，我们的引导法线滤波能够很好地保留几何特征，并且对复杂的表面形状具有鲁棒性。在各种网格上进行的大量实验在视觉和定量上都显示了我们方法的优越性。
