COMPARISON AND ASSESSMENT OF 3D REGISTRATION AND GEOREFERENCING APPROACHES OF POINT CLOUDS IN THE CASE OF EXTERIOR AND INTERIOR HERITAGE BUILDING RECORDING

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Murtiyoso, A.^1¹ 

[1] Photogrammetry and Geomatics Group, ICube Laboratory UMR 7357, INSA Strasbourg, France^1

关键词: Point Cloud;    3D Registration;    Georeferencing;    Exterior;    Interior;    Heritage Buildings;    UAV Photogrammetry;    TLS;    Assessment;    PCL;   

DOI  :  10.5194/isprs-archives-XLII-2-745-2018

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

In the field of 3D heritage documentation, point cloud registration is a relatively common issue. With rising needs for Historic Building Information Models (HBIMs), this issue has become more important as it determines the quality of the data to be used for HBIM modelling. Furthermore, in the context of historical buildings, it is often interesting to document both the exterior façades as well as the interior. This paper will discuss two approaches of the registration and georeferencing of building exterior and interior point clouds coming from different sensors, namely the independent georeferencing method and the free-network registration and georeferencing. Building openings (mainly windows) were used to establish common points between the systems. These two methods will be compared in terms of geometrical quality, while technical problems in performing them will also be discussed. Furthermore, an attempt to automate some parts of the workflow using automatic 3D keypoints and features detection and matching will also be described in the paper. Results show that while both approaches give similar results, the independent approach requires less work to perform. However, the free-network method has the advantage of being able to compensate for any systematic georeferencing error on either system. As regards to the automation attempt, the use of 3D keypoints and features may reduce processing time; however correct tie point correspondence filtering remains difficult in the presence of heavy point cloud noise.

【 授权许可】

CC BY