Outlier formation and removal in 3D laser scanned point clouds

Abstract

3D scanners have become widely used in many industrial applications in reverse engineering, quality inspection, entertainment industry, etc. Despite the popularity of 3D scanners, the raw scanned data, referred to as point cloud, is often contaminated by outliers not belonging to the scanned surface. Moreover, when the scanned surface is highly reflective, outliers become much more extensive due to specular reflections. Such outliers cause considerable issues to point cloud applications and thus need to be removed through an outlier detection process. Considering the commonness of reflective surfaces in mechanical parts, it is critical to investigate the outlier formation mechanism and develop methods to effectively remove outliers. However, research on how outliers are formed in scanning reflective surfaces is very limited. Meanwhile, existing outlier removal methods show limited effectiveness in detecting extensive outliers. This thesis investigates the outlier formation mechanism in scanning reflective surfaces using laser scanners, and develops outlier removal algorithms to effectively and efficiently detect outliers in the scanned point clouds. The overall objective is to remove outliers in a raw data to obtain a clean point cloud in order to ensure the performance of point cloud applications. In particular, two outlier formation models, mixed reflections and multi-path reflections, are proposed and verified through experiments. The effects of scanning orientation on outlier formation are also experimentally investigated. A guidance of proper scan path planning is provided in order to reduce the occurrence of outliers. Regarding outlier removal, a rotating scan approach is proposed to efficiently remove view-dependent outliers. A flexible and effective algorithm is also presented to detect the challenging non-isolated outliers as well as other outliers.