A review of algorithms for filtering the 3D point cloud

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.