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.

来自扫描仪或立体视觉的点云不可避免地包含对后续程序产生负面影响的异常值。以前的工作根据异常值的特征对异常值进行分类，指导有针对性的异常值去除设计方法。因此，异常值分类对于相应的方法和异常值去除效果至关重要。所提出的基于类型的异常值去除框架（TBORF）旨在通过考虑基础点云和异常值的特征来更精细地对异常值进行分类。因此，设计的异常值去除方法可以更有针对性。为此，该框架首先使用三个建议的指标量化输入点云的特征。根据这个定量结果，输入点云分为四种类型。同时，提出了三种新的单准则方法来提高对特定类型的异常值去除的效果。基于点云分类和提出的单准则方法，为每种类型的点云精心设计了适当的组合方法。室外和室内点云数据集的性能评估表明，TBORF 可以有效地去除各种异常值，促进后续的数字几何处理操作。