# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 2 Literature Review
### Measurement of Object Dimensions Using Point Clouds  
Measuring object dimensions from point clouds is a critical component of the Scan-to-BIM process, as it directly impacts the geometric accuracy of the resulting as-built BIMs. One of the most widely used and robust methods for extracting geometric primitives, such as lines and planes, is the RANSAC algorithm. [PERSON] et al. [31] proposed a BIM generation method using a morphological approach. They used RANSAC to determine planar primitive segmentation to extract vertical and horizontal planes of each room region. [PERSON] et al. [32] identified roof bolts in MLS point clouds which captured the mine roadway. RANSAC algorithm was adopted to extract cylinder fitted mesh of roof bolts. [PERSON] et al. [33] used RANSAC to obtain representative lines of wall components in 2D projected point clouds. In addition, RANSAC algorithm was utilized to obtain representative planes or lines of objects from point clouds [34; 35; 36; 37; 38; 39; 40; 41]. Thus, RANSAC has been widely adopted in Scan-to-BIM studies, as it fits the optimal lines or planes while accounting for outliers in the input point clouds.  
Several studies have employed approaches that differ from the conventional RANSAC algorithm. [PERSON] et al. [42] proposed a novel method for planar surface detection in MLS point clouds. This method utilizes scan profile patterns and planarity values between neighboring scan profiles to detect and segment planar surfaces. It demonstrated higher performance compared to RANSAC. [PERSON] et al. [6] extracted representative object lines by leveraging normal vector-based tangent plane estimation and region-growing facet generation from 3D point clouds. [PERSON] et al. [43] extracted indoor structure planes and lines using morphological processing with a 2D binary map and skeletonization. [PERSON] et al. [44] extracted representative planes and lines of indoor structures by coupling point cloud completion with surface connectivity inference, leveraging truncated signed distance function (TSDF) octree-based visibility estimation and normal-based region growing. [PERSON] et al. [45] proposed an advanced method using modified ring-stepping clustering to extract lines from the xy-plane of main building components.  
In summary, RANSAC has been the most frequently used and reliable method for extracting object lines and planes from point clouds. Additionally, approaches such as neighboring scan profiles, normal vectors, 2D binary maps, and surface connectivity inference have also been explored. These methods effectively extract representative lines or planes from objects captured in point clouds. While these methods extract accurate lines or planes, they overlook the potential bias in the point cloud distribution, which may significantly impact the geometric accuracy of as-built BIMs. In contrast, little research has explicitly addressed the analysis of error distribution patterns in MLS point clouds. Therefore, this study aims to bridge this gap by providing an in-depth analysis of the error distribution patterns in MLS-derived point clouds.