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Feature-preserving mesh denoising based on guided normal filtering

机译：基于导引法向滤波的特征保留网格去噪

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摘要

In order to robustly perform tasks based on 3D data model, we propose a feature-preserving mesh denoising algorithm based on the face classification. In the proposed algorithm, the sharp features which play a key role in 3D models are kept unchanged while denoising. The multiscale tensor voting is used to classify the faces into two classes where one is called as feature faces and another as non-feature faces. Feature faces is usually distributed in the neighbourhood of shape edges. Thus these feature faces are key faces in perceptual quality. For processing the faces more efficiently, we propose a search algorithm to find those faces which are close to the feature face and are of similar geometrical properties and then use them to guide the filtering process. The remaining faces are processed by an iteratively joint bilateral filtering. Finally, vertex position is updated according to the filtered face normals. the effectiveness of proposed approach is validated through extensive experiments. Experimental results show the performance is better than the existing methods.
机译：为了可靠地执行基于3D数据模型的任务，我们提出了一种基于人脸分类的保留特征的网格去噪算法。在提出的算法中，在3D模型中起关键作用的尖锐特征在去噪时保持不变。多尺度张量投票用于将面孔分类为两类，其中一类称为特征面孔，另一类称为非特征面孔。特征面通常分布在形状边缘附近。因此，这些特征面是感知质量的关键面。为了更有效地处理人脸，我们提出了一种搜索算法，以查找与特征人脸接近且具有相似几何特性的人脸，然后使用它们来指导过滤过程。其余的面孔通过迭代联合双边过滤处理。最后，根据过滤后的脸部法线更新顶点位置。通过大量实验验证了所提方法的有效性。实验结果表明，该方法性能优于现有方法。

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