LINE-BASED REFERENCING BETWEEN IMAGES AND LASER SCANNER DATA FOR IMAGE-BASED POINT CLOUD INTERPRETATION IN A CAD-ENVIRONMENT

摘要

Photogrammetric 3D object reconstruction can be obtained from images or laser scanner data. Because of their complementary characteristics, these data are suitable for a combined interpretation. In this context, a new option for 3D object reconstruction appears. 3D geometries can be obtained by monoplotting-like procedures, mapping in monocular images and deriving depth information from the laser scanner data. For this purpose, a correct geometric referencing of the data is required. The referencing primarily contains the determination of the exterior orientation of a single image towards a laser scanner point cloud. In architectural applications, linear features can often be extracted easier than points. Hence, the paper describes two methods for image orientation based on straight line features. Both approaches are based on the collinearity equations. The first one uses correspondences between image points and 3D lines extracted from the point cloud, whereas the second approach uses linear features in image space and point cloud. These methods were tested with two different data sets and compared to a classical photo resection using points. For the first data set, a test field with a large number of targets, the results were also compared to the results of a bundle block adjustment. The second data set is a building facade, where natural features rather than signalised points were the basis of image orientation. The results show a different accuracy potential for the three methods. As expected, the best results were obtained with the point-based photo resection, followed by the line-based method with the point-to-line correspondence. But it is also shown that using lines instead of discrete points may be a valuable option for the orientation of single digital images to laser scanner 3D point clouds.