关键词: 三维点云, 法向量估计, 噪声, 外点, 尖锐特征

Abstract: Point clouds are becoming more and more common for the representation of 3D geometry models because of its advantages over mesh models，such as easy acquisition，straightforward representation and flexibility.Since normal is one of the essential properties of point clouds，the estimation of normal plays an important role in point clouds processing.However，point clouds are prone to contain noise，outliers and holes because of the unavoidable noise，physical errors and occlusions during acquisition.Moreover，some point clouds，such as data from CAD models，also contain sharp features.These factors pose different challenges to normal estimation.A comprehensive survey of the recent work in normal estimation is presented，and the principles as well as key techniques of them are discussed with emphases on the ability of dealing with noise，outliers，sharp features，etc.In the end，the conclusions and the future research trends of this topic are given.

Key words: 3D point clouds, normal estimation, noise, outlier, sharp features