Robust Feature Graph for Point Cloud Denoising

Abstract

Point cloud is an important and commonly used signal representation for volume objects or scenes in the real world. Due to the imperfect acquisition of the point cloud, there is nonnegligible noise in the point cloud. Most literatures that use graph signal processing (GSP) for point cloud denoising (PCD) generally construct k-NN graph to represent the point cloud. However, the graph constructed based on this scheme can not compactly represent the underlying structure of a noisy point cloud. In this paper, we propose a feature graph that can effectively and naturally represent the structure of the point cloud. To construct the feature graph, a feature sampling method is exploited to obtain the feature points. Then, patches are built based on the feature points. After that, the feature graph is constructed by connecting all the points in the patches. Finally, we apply the feature graph to the PCD problem and exploit graph Laplacian regularization (GLR) as smoothing prior information for denoising. Experimental results show that our proposed PCD method not only outperforms the existing PCD methods in objective evaluation metrics, but also performs better in processing the inner and edge structure of the point cloud.