Approach for the Semi-Automatic Verification of 3d Building Models

Abstract

ABSTRACT:In the field of spatial sciences, there are a large number of disciplines and techniques for capturing data to solve a variety of different tasks and problems for different applications. Examples include: traditional survey for boundary definitions, aerial imagery for building models, and laser scanning for heritage facades. These techniques have different attributes such as the number of dimensions, accuracy and precision, and the format of the data. However, because of the number of applications and jobs, often over time these data sets captured from different sensor platforms and for different purposes will overlap in some way. In most cases, while this data is archived, it is not used in future applications to value add to the data capture campaign of current projects. It is also the case that newly acquire data are often not used to combine and improve existing models and data integrity. The purpose of this paper is to discuss a methodology and infrastructure to automatically support this concept. That is, based on a job specification, to automatically query existing and newly acquired data based on temporal and spatial relations, and to automatically combine and generate the best solution. To this end, there are three main challenges to examine; change detection, thematic accuracy and data matching.

Quality evaluation of 3D city building Models with automatic error diagnosis

“…On the other hand, external data is used to estimate the correctness of the reconstruction. Such data can be very accurate field surveys or very high density terrestrial laser scanning point clouds, similarly to the semi-automatic approach proposed by Helmholz et al (2013). Oblique aerial images can be a more cost-effective solution.…”

Grammar-based Automatic 3D Model Reconstruction from Terrestrial Laser Scanning Data

“…In order to cover the requirements and to keep the existing models up to date, automatic reconstruction methods are needed to avoid the labour intensive and time comsuming manual processing workflow. While in recent years a number of paper were published focusing on 2D data sets (Helmholz et al, 2013), there are two major types of model reconstruction approaches in principle (Musialski et al, 2012), which are the data-driven approach and the model-driven approach. A data-driven approach extracts features and elements (e.g.…”