Multiple Point Clouds Data Fusion Method for 3D City Modeling

Three-dimensional city models in multiple levels of details consists of the fundamental geospatial data infrastructure for digital city and wisdom society, feature based interactive modeling using sparse point or line features and automatic modeling based on dense point clouds have triggered interests in both academic and industrial communities. Because of the complexity of spatial structure of three-dimensional cities, fusion of multi-source, multi-view and multi-temporal point clouds is a critical issue of three-dimensional city modeling. The basic idea is to integrate multiple point clouds data, which have different characteristics such as angle of view, density, accuracy, scale, level of detail, time stamps, into the same coherent representation. This paper first summarizes the main characterstics of ubiquitous point clouds data, and then analyzes the major trend of multiple point clouds data fusion methods from three aspects, time-space datum and precision, scale, and semantics. Finally, critical issues of multiple point clouds data fusion for 3D city modeling are given.