3D object extraction of high-resolution 3D point clouds

National Survey Departments acquire area-wide, controlled airborne laser scanning (ALS) datasets with different point densities, which are at least classified into ground and non-ground points. The Working Committee of the Surveying Authorities of the Laender of the Federal Republic of Germany (AdV) is discussing about an update cycle of 10 years for ALS point clouds. The national survey departments also acquire 3D point clouds from aerial images every 2-3 years with high overlapping ratios using a method called Dense Image Matching (DIM). Those DIM point clouds have a high point density, which is equal to the original aerial image resolution. In addition, those DIM point clouds also contain radiometric information from the aerial images, but only reconstruct the surface due to image correlation. This project is split into four distinctive topics.

Fusion of datasets from different sensor systems

ALS point clouds have a high point-wise horizontal and vertical accuracy, while DIM point clouds have a high geometrical resolution and additional radiometric information. Both data types have to be registered concerning those different attributes as well as additional criteria.

Updating previous point clouds with newly acquired datasets

Once both point clouds are registered, the point cloud can be fused considering the individual quality and resolution of each point cloud. Besides a fusion of both data sets into one high quality and resolution point cloud, both point clouds could also be used for change detection.

Classification of point clouds

To further process digital terrain and surface models from these point clouds, they are normally classified into ground, non-ground and others. Others contain dynamic objects such as vehicles, which are neither part of the digital terrain nor surface model. Based on the accuracy and the additional high radiometric and geometric resolution of the DIM point clouds, the amount of classes should be extended according to the AdV-standards. In addition, those classified point clouds can further improve the registration, fusion and change detection task.

3D line and object extraction and texturizing

Once the 3D point clouds are enhanced according to the geometry as well as information depth using the radiometric information, this point cloud can be used to extract 3D lines, planes and objects. 3D objects should be modelled according to the AAA-catalogue and finally be texturized using the original RGB values from the aerial images.