Abstract
In this paper, a primitive-based 3D building roof modeling method, by integrating LiDAR data and aerial imagery, is proposed. The novelty of the proposed modeling method is to represent building roofs by geometric primitives and to construct a cost function by using constraints from both LiDAR data and aerial imagery simultaneously, so that the accuracy potential of the different sensors can be tightly integrated for the building model generation by an integrated primitive’s parameter optimization procedure. To verify the proposed modeling method, both simulated data and real data with simple buildings provided by ISPRS (International Society for Photogrammetry and Remote Sensing), were used in this study. The experimental results were evaluated by the ISPRS, which demonstrate the proposed modeling method can integrate LiDAR data and aerial imagery to generate 3D building models with high accuracy in both the horizontal and vertical directions. The experimental results also show that by adding a component, such as a dormer, to the primitive, a variant of the simple primitive is constructed, and the proposed method can generate a building model with some details.
Highlights
Developing a full automatic algorithm that can generate highly accurate building models is still a challenging task [1].Traditionally, photogrammetry is the primary approach for deriving geo-spatial information through the use of single or multiple optical images
To evaluate the reconstruction accuracy of the proposed method, a simulation experiment and a real data experiment utilizing a data set provided by ISPRS (International Society for Photogrammetry and Remote Sensing) were conducted, which will be described in detail
To combine the strengths of these two data sources for the generation of building roof models, a primitive-based 3D building roof modeling method is proposed in this study
Summary
Developing a full automatic algorithm that can generate highly accurate building models is still a challenging task [1]. Photogrammetry is the primary approach for deriving geo-spatial information through the use of single or multiple optical images. Many studies have been conducted for 3D building model reconstruction by using aerial imagery or LiDAR (Light Detection and Ranging) data. When aerial imagery were used only for the building reconstruction, the low degree of automation during the matching process is the main limitation, especially when occlusions are present [9]. Some new dense matching algorithm, such as semi-global matching and patched based matching were developed to generate dense point cloud of buildings, but the representation of these buildings is based on a TIN (Triangulated Irregular Network), which cannot deliver the semantic information of each building
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