Chen Honghua, Wei Mingqiang, Sun Yangxing, Xie Xingyu, Wang Jun
IEEE Trans Vis Comput Graph. 2020 Nov;26(11):3255-3270. doi: 10.1109/TVCG.2019.2920817. Epub 2019 Jun 4.
Point cloud is the primary source from 3D scanners and depth cameras. It usually contains more raw geometric features, as well as higher levels of noise than the reconstructed mesh. Although many mesh denoising methods have proven to be effective in noise removal, they hardly work well on noisy point clouds. We propose a new multi-patch collaborative method for point cloud denoising, which is solved as a low-rank matrix recovery problem. Unlike the traditional single-patch based denoising approaches, our approach is inspired by the geometric statistics which indicate that a number of surface patches sharing approximate geometric properties always exist within a 3D model. Based on this observation, we define a rotation-invariant height-map patch (HMP) for each point by robust Bi-PCA encoding bilaterally filtered normal information, and group its non-local similar patches together. Within each group, all patches are geometrically similar, while suffering from noise. We pack the height maps of each group into an HMP matrix, whose initial rank is high, but can be significantly reduced. We design an improved low-rank recovery model, by imposing a graph constraint to filter noise. Experiments on synthetic and raw datasets demonstrate that our method outperforms state-of-the-art methods in both noise removal and feature preservation.
点云是三维扫描仪和深度相机的主要数据来源。它通常包含更多原始几何特征，并且比重建网格具有更高的噪声水平。尽管许多网格去噪方法已被证明在去除噪声方面有效，但它们在有噪声的点云上很难取得良好效果。我们提出了一种新的用于点云去噪的多补丁协作方法，该方法被作为低秩矩阵恢复问题求解。与传统的基于单补丁的去噪方法不同，我们的方法受到几何统计的启发，几何统计表明在一个三维模型中总是存在许多具有近似几何属性的表面补丁。基于这一观察，我们通过鲁棒双主成分分析（Bi-PCA）对双边滤波后的法线信息进行编码，为每个点定义一个旋转不变的高度图补丁（HMP），并将其非局部相似补丁分组在一起。在每个组内，所有补丁在几何上相似，但都受到噪声影响。我们将每个组的高度图打包成一个HMP矩阵，其初始秩很高，但可以显著降低。我们通过施加图约束来设计一个改进的低秩恢复模型以过滤噪声。在合成数据集和原始数据集上的实验表明，我们的方法在去除噪声和保留特征方面均优于现有方法。
