Sun Xianfang, Rosin Paul, Martin Ralph, Langbein Frank
School of Computer Science, Cardiff University, Cardiff, UK.
IEEE Trans Vis Comput Graph. 2007 Sep-Oct;13(5):925-38. doi: 10.1109/TVCG.2007.1065.
We present a simple and fast mesh denoising method, which can remove noise effectively, while preserving mesh features such as sharp edges and corners. The method consists of two stages. Firstly, noisy face normals are filtered iteratively by weighted averaging of neighboring face normals. Secondly, vertex positions are iteratively updated to agree with the denoised face normals. The weight function used during normal filtering is much simpler than that used in previous similar approaches, being simply a trimmed quadratic. This makes the algorithm both fast and simple to implement. Vertex position updating is based on the integration of surface normals using a least-squares error criterion. Like previous algorithms, we solve the least-squares problem by gradient descent, but whereas previous methods needed user input to determine the iteration step size, we determine it automatically. In addition, we prove the convergence of the vertex position updating approach. Analysis and experiments show the advantages of our proposed method over various earlier surface denoising methods.
我们提出了一种简单快速的网格去噪方法，该方法能够有效去除噪声，同时保留诸如尖锐边缘和角点等网格特征。该方法包括两个阶段。首先，通过对相邻面法线进行加权平均来迭代过滤有噪声的面法线。其次，迭代更新顶点位置以与去噪后的面法线一致。法线过滤过程中使用的权重函数比以前类似方法中使用的要简单得多，只是一个截断二次函数。这使得该算法既快速又易于实现。顶点位置更新基于使用最小二乘误差准则对面法线进行积分。与以前的算法一样，我们通过梯度下降来解决最小二乘问题，但以前的方法需要用户输入来确定迭代步长，而我们自动确定它。此外，我们证明了顶点位置更新方法的收敛性。分析和实验表明，我们提出的方法优于各种早期的曲面去噪方法。
