True 3D building reconstruction: façade, roof and overhang modelling from oblique and vertical aerial imagery

Abstract

Aerial imaging systems increasingly gain oblique viewing capabilities. Through these passive systems, photogrammetric 3D point clouds of a scene become available in addition to traditional vertical 2.5D information. In the field of urban reconstruction, this complementary information seeks for robust and automated fusion methods in order to derive 3D building geometry as well as topology in larger scales. It is sequentially shown how to get from façade planes over building footprints to roof reconstruction including overhangs. Façade planes are extracted from a photogrammetric high-resolution 3D point cloud. Local regression methods in 2D space are used to determine the local direction and a criterion for the local linearity of the point cloud. Based on these two parameters, the 3D point cloud is segmented according to which façade it belongs to. From the segmented point cloud, building footprints are extracted as polygons. Similar to cadaster information, those polygons, along with a traditional digital surface model (DSM), serve for one thing as the basis for overhang determination which is performed by fitting polynoms on the outside of façades and using their inflection points as overhang boundary. For another thing, they serve as roof areas which are segmented, topologically described and geometrically modelled. Again local regression methods are used but this time in 3D space in order to segment roof parts. Subsequently, the roof topology is derived using region growing methods. The final building models hold both, geometrical and topological properties.

Accurate Reconstruction of the LoD3 Building Model by Integrating Multi-Source Point Clouds and Oblique Remote Sensing Imagery

“…However, they are simply integrated as textured meshes and lack semantic knowledge. Some researchers have tried to use sequential images to model the building as piecewise planar facades [19][20][21][22][23][24]. Unfortunately, the problems of shadow, occlusion and texture lacking exist in the image data, resulting in both geometric distortion in local area and low accuracy.…”

Comparison Between Two Generic 3d Building Reconstruction Approaches – Point Cloud Based vs. Image Processing Based

“…The presented point cloud based approach is already used for reconstructing roof structures with help of a priori knowledge from cadaster maps (Dahlke et al, 2015). This published approach lacks the capability of reconstructing vertical structures like façades or vertical roof structures.…”

An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds

“…Since the calculated distance and angle from point P 0 to the facet are below the iterative densification threshold, point P 0 will be added to the TIN and the TIN is updated. We continue to densify the TIN, and then point P. 4 is accepted by the TIN, as shown in Figure 2c. When the number of points added into the TIN increases, the distance between the projected point of the newly added unclassified ground point and the vertices of the facet become small.…”

“…Point clouds (including light detection and ranging (LiDAR) point clouds and dense image matching (DIM) point clouds have been widely used in various fields, such as land cover classification [1], canopy detection and vegetation analysis [2,3], the reconstruction of digital terrain models (DTM) [4], etc. In these applications, point clouds filtering is an essential step.…”