Quality evaluation of point cloud model for interior structure of a common building

Abstract

This paper presents a standardized quality criteria to evaluate the 3D point cloud model of the indoor building which is based on point cloud's data accuracy, the prior characteristics of the building and the coincidence errors of the point cloud model. Our assessment framework involves three steps: the point cloud data acquisition, model generation and quality evaluation. In model generation progress, incapacity of scanning the whole building information one time since its multi-storied spatial structure, the building model need to be registered and merged. In evaluation step, taking into account the need of mapping, indoor location and navigation, the establishment of an interior spatial building model requires accurate measurement. Therefore, we adopt data noise analysis to give a judgement. Then, since the geometric characteristics of the building model are varying, the geometric analysis is proposed to evaluate acquisition errors and registration error. Comparative experiments demonstrate our method give integrate, realistic and reliable quality framework for the indoor building point cloud model.