The paper reports some comparisons between commercial software able to automatically process image datasets for 3D reconstruction purposes. The main aspects investigated in the work are the capability to correctly orient large sets of image of complex environments, the metric quality of the results, replicability and redundancy. Different datasets are employed, each one featuring a diverse number of images, GSDs at cm and mm resolutions, and ground truth information to perform statistical analyses of the 3D results. A summary of (photogrammetric) terms is also provided, in order to provide rigorous terms of reference for comparisons and critical analyses.

Accurate 3D city models represent an important source of geospatial information to support various “smart city” applications, such as space management, energy assessment, 3D cartography, noise and pollution mapping as well as disaster management. Even though remarkable progress has been made in recent years, there are still many open issues, especially when it comes to the 3D modelling of complex urban scenarios like historical and densely-built city centres featuring narrow streets and non-conventional building shapes. Most approaches introduce strong building priors/constraints on symmetry and roof typology that penalize urban environments having high variations of roof shapes. Furthermore, although oblique photogrammetry is rapidly maturing, the use of slanted views for façade reconstruction is not completely included in the reconstruction pipeline of state-of-the-art software. This paper aims to investigate state-of-the-art methods for 3D building modelling in complex urban scenarios with the support of oblique airborne images. A reconstruction approach based on roof primitives fitting is tested. Oblique imagery is then exploited to support the manual editing of the generated building models. At the same time, mobile mapping data are collected at cm resolution and then integrated with the aerial ones. All approaches are tested on the historical city centre of Bergamo (Italy).

This paper discusses a methodology to evaluate the precision and the accuracy of a commercial Mobile Mapping System (MMS) with advanced statistical methods. So far, the metric potentialities of this emerging mapping technology have been studied in few papers, where generally the assumption that errors follow a normal distribution is made. In fact, this hypothesis should be carefully verified in advance, in order to test how well the Gaussian classic statistics can adapt to datasets that are usually affected by asymmetrical gross errors. The workflow adopted in this study relies on a Gaussian assessment, followed by an outlier filtering process. Finally, non-parametric statistical models are applied, in order to achieve a robust estimation of the error dispersion. Among the different MMSs available on the market, the latest solution provided by RIEGL is here tested, i.e. the VMX-450 Mobile Laser Scanning System. The test-area is the historic city centre of Trento (Italy), selected in order to assess the system performance in dealing with a challenging and historic urban scenario. Reference measures are derived from photogrammetric and Terrestrial Laser Scanning (TLS) surveys. All datasets show a large lack of symmetry that leads to the conclusion that the standard normal parameters are not adequate to assess this type of data. The use of non-normal statistics gives thus a more appropriate description of the data and yields results that meet the quoted a-priori errors.