Repositioning Technique Based on 3D Model Using a Building Shape Registration Algorithm

Abstract

The demand for 3D spatial data and effective dataset construction has been increasing. However, the construction of 3D spatial datasets is more time-consuming and costlier than that of 2D spatial data. In addition, maintaining 3D city models up to date long after their initial construction is difficult. In this study, we developed a method of updating 3D building information within a relatively short time frame using highly accurate 2D building information. This method can be used to correct 3D building information automatically. Unmanned aerial vehicle (UAV) imagery of the study area was obtained, and a 3D model was developed using commercial software. Subsequently, the 3D model information was mapped onto the 2D information. After transforming the paired objects into point data at a set interval, registration parameters were calculated by applying the iterative closest-point technique. The calculated parameters were used to reposition the 3D model, enabling the creation of a model that overlaps with more than 98% of the existing spatial information data. Thus, it was confirmed that 3D building models can be produced without ground control points and can be readily updated at low cost.

Enhancing Indoor Navigation in Intelligent Transportation Systems with 3D RIF and Quantum GIS

“…Similarly, a research investigation presented a repositioning methodology that relies on a 3D model incorporating a building shape registration algorithm. This strategy facilitates the revision of 3D building data by leveraging exceptionally precise 2D information [18].…”