Type-based outlier removal framework for point clouds

Abstract

Point clouds from scanners or stereo vision inevitably contain outliers which have negative effects on subsequent procedures. Previous works classify outliers according to their characteristics, which guide the design of targeted outlier removal methods. Thus, outlier classification is critical to the corresponding method and outlier removal effect. The proposed type-based outlier removal framework (TBORF) aims to classify outliers more elaborately by considering both the characteristics of the underlying point cloud and the outliers. Therefore, the designed outlier removal methods can be more targeted. To this end, the framework first quantifies the characteristics of the input point cloud using three proposed metrics. According to this quantitative result, the input point clouds are classified into four types. Meanwhile, three new single-criterion methods are proposed to improve the effect on specific types of outlier removal. Based on the point cloud classification and the proposed single-criterion methods, an appropriate combined method is carefully designed for each type of point cloud. Performance evaluations on both outdoor and indoor point cloud datasets demonstrate that TBORF can effectively remove various outliers, facilitating subsequent digital geometry processing operations.