Robust Normal Estimation of Point Cloud with Sharp Features via Subspace Clustering

摘要

Normal estimation is an essential step in point cloud based geometric processing, such as high quality point based rendering and surface reconstruction. In this paper, we present a clustering based method for normal estimation which preserves sharp features. For a piecewise smooth point cloud, the k-nearest neighbors of one point lie on a union of multiple subspaces. Given the PCA normals as input, we perform a subspace clustering algorithm to segment these subspaces. Normals are estimated by the points lying in the same subspace as the center point. In contrast to the previous method, we exploit the low-rankness of the input data, by seeking the lowest rank representation among all the candidates that can represent one normal as linear combinations of the others. Integration of Low-Rank Representation (LRR) makes our method robust to noise. Moreover, our method can simultaneously produce the estimated normals and the local structures which are especially useful for denoise and segmentation applications. The experimental results show that our approach successfully recovers sharp features and generates more reliable results compared with the state-of-the-art.