A review of algorithms for filtering the 3D point cloud

• Published in: Signal processing. Image communication Vol. 57; pp. 103 - 112
• Main Authors: Han, Xian-Feng, Jin, Jesse S., Wang, Ming-Jie, Jiang, Wei, Gao, Lei, Xiao, Liping
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.09.2017; Elsevier BV
• Subjects: 3-D technology; 3D point cloud; Algorithms; Computing time; Feature-preserving; Filtering methods; Filtration; Noise reduction; Outliers (statistics); Robustness (mathematics); Studies; 3D point cloud; Noise reduction; Filtering methods; Feature-preserving
• ISSN: 0923-5965; 1879-2677
• DOI: 10.1016/j.image.2017.05.009

In recent years, 3D point cloud has gained increasing attention as a new representation for objects. However, the raw point cloud is often noisy and contains outliers. Therefore, it is crucial to remove the noise and outliers from the point cloud while preserving the features, in particular, its fine details. This paper makes an attempt to present a comprehensive analysis of the state-of-the-art methods for filtering point cloud. The existing methods are categorized into seven classes, which concentrate on their common and obvious traits. An experimental evaluation is also performed to demonstrate robustness, effectiveness and computational efficiency of several methods used widely in practice. •To the best of our knowledge, this is the first review paper in the literature that focuses on the filtering algorithms for 3D point cloud at present.•A comprehensive review of the state-of-the-art filtering methods for 3D point clouds is summarized.•Experiments concerning on performance comparison of several widely used approaches are carried out.