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论文类型：期刊论文

发表时间：2018-02-01

发表刊物：Journal of Computational and Applied Mathematics

收录刊物：SCIE、EI、CPCI-S

卷号：329

页面范围：57-67

ISSN号：03770427

关键字：Normal estimation; Point cloud; Neighborhood shift

摘要：For accurately estimating the normal of a point, the structure of its neighborhood has to be analyzed. All the previous methods use some neighborhood centering at the point, which is prone to be sampled from different surface patches when the point is near sharp features. Then more inaccurate normals or higher computation cost may be unavoidable. To conquer this problem, we present a fast and quality normal estimator based on neighborhood shift. Instead of using the neighborhood centered at the point, we wish to locate a neighborhood containing the point but clear of sharp features, which is usually not centering at the point. Two specific neighborhood shift techniques are designed in view of the complex structure of sharp features and the characteristic of raw point clouds. The experiments show that our method out-performs previous normal estimators in either quality or running time, even in the presence of noise and anisotropic sampling. ? 2017 Elsevier B.V.