Automatic 3d Building Reconstruction from a Dense Image Matching Dataset

Over the last 20 years the demand for three dimensional (3D) building models has resulted in a vast amount of research being conducted in attempts to automate the extraction and reconstruction of models from airborne sensors. Recent results have shown that current methods tend to favour planar fitting procedures from lidar data, which are able to successfully reconstruct simple roof structures automatically but fail to reconstruct more complex structures or roofs with small artefacts. Current methods have also not fully explored the potential of recent developments in digital photogrammetry. Large format digital aerial cameras can now capture imagery with increased overlap and a higher spatial resolution, increasing the number of pixel correspondences between images. Every pixel in each stereo pair can also now be matched using per-pixel algorithms, which has given rise to the approach known as dense image matching. This paper presents an approach to 3D building reconstruction to try and overcome some of the limitations of planar fitting procedures. Roof vertices, extracted from true-orthophotos using edge detection, are refined and converted to roof corner points. By determining the connection between extracted corner points, a roof plane can be defined as a closed-cycle of points. Presented results demonstrate the potential of this method for the reconstruction of complex 3D building models at CityGML LoD2 specification.