The potentialities of the use of the UAV survey as a base for the generation of the context mesh are illustrated through the experiments on the case study, the Crotone Fortress, proposing a systematic general methodology and two procedural workflows for the importation of the triangulated model, maintaining its real geographical coordinates, in the Autodesk Revit environment through a Dynamo Visual Programming script. Given a certain model quality requirement, the required data quality can be inferred from the analysis results, which can help achieve a better planned scan-to-BIM process. The analysis results show that DOC and PD play different roles in influencing recognizability and various modeling errors. Using these measures, the relationship between data quality and model quality was examined regarding significance and correlation. The data quality was measured using Degree of Completeness (DOC) and Point Density (PD), and model quality was measured using recognizability, radius error (for pipe), width error (for duct), and length error. Afterwards, the data quality and model quality were measured for each MEP member.
Then, each point cloud dataset was converted into a BIM model by a BIM modeler. Two MEP scenes were scanned with different scanning settings (angular resolutions and scanning locations), and a total of 20 point cloud datasets were obtained. Hence, this study aims to understand the relationship between data quality and model quality using a case study on mechanical, electrical and plumbing (MEP) scenes. However, there has been little research that investigates the relationship between data quality and model quality in a quantitative manner. data quality) greatly affects the quality of the generated BIM model (i.e.
In scan-to-BIM process, the quality of scan data (i.e.
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