Airborne And Terrestrial LIDAR Data Fusion For 3D Building Reconstruction

There are increasing demands in 3D models of City buildings in many fields. However, the efficiency of 3D building modeling is not high enough to catch up with the huge demands. This paper present a integrated method which fuse the newly emerging airborne and terrestrial LIDAR data. By this method, we combine the extracted roof and the facade of the building for a better 3D model of buildings.This paper summarizes and analyzes the past related work. Based on the the overview, the problems and shortcomings of existing methods are mastered. Then based on the special property of LIDAR data, we use a point cloud segmentation and shape/split grammar as the key ideas for the reconstruction of city buildings. Also, we proposed a set of fusion method for airborne and terrestrial LIDAR data to reconstruct the buildings in urban area. The proposed construction theory is aimed to build high detailed building facade. Finally, we push on a experiment to make the avalibility of our process and method. The main research work are summarized as follows:(1) Terrestrial LIDAR equipment working principal is deeple learned, and the scan line reconstruction is carried out based on the working principle, and then the topology of points is reconstructed under the scanning line. A point cloud image is generated according to the topology of points in a single-station LIDAR data, Based on point cloud image, we can draw a large number of existing mature image processing algorithms for processing LIDAR data, this image based on a series of point cloud data processing methods, such as normal vector calculation, point cloud segmentation, and verified by experiments the method is effective and efficient.(2) The building detecting from airborne LIDAR data is done by a machine learning methods. Using the local differential geometric properties to measure the top of their data is the possibility of building, combined with support vector machines to the roof surface coefficient differential geometric properties of data for training, which are building top of the data detection model. LIDAR data from the ground to detect the building data, the least squares plane fitting residuals as the LIDAR data, the flatness of the mid-point measure, to distinguish the rules of distribution of LIDAR data in the data and unstructured data. Then the normal vector smoothness constraint method to segment the point cloud, point cloud data can be split into multiple rules set, and then through the restriction point set of a variety of attributes identified from the rules for building data point set.(3) combined with existing geo-spatial positioning of resources, a semi-automatic airborne and ground LIDAR data registration method. Access to the ground in the interactive data scanning LIDAR site in airborne data in the approximate location, respectively, by two-step solution to seek two kinds of registration is rotation and translation parameters, the first compendium of data at the corresponding position two Select a range of point cloud fitting plane through the two corresponding plane to determine the translation parameters, and then scanning the ground near the site automatically searches for airborne LIDAR data in the roof of an empty profile, and ground LIDAR data in the facade, Use empty profile extracted from the airborne and ground LIDAR building facade building facade data matching, the rotation parameters obtained registration to complete airborne and ground LIDAR data registration, and by plane between angle on the registration accuracy was analyzed.(4) using neighbor method similar to clustering vector data, the idea of the roof surface film division, while using regular grid point nearest to extract the contour patches, the flat top type roof, the direct use of data-driven method, the contour lines to vote on the rules of linear and combined to generate the roof model, the existence of pyramid-shaped roof, the roof or through the surface film of the topology and the surface film and the angle between the horizontal plane to generate the shape grammar, by the shape grammar roof surface model generation.(5) defines the various elements of the building facade as well as the many unique properties of the relationship between the use of these rules binding relations between the surface of the segmented type of film for identification, identified a number of facade elements. And use Rules grid method the windows of the building facade was extracted by the window size, location and type. From the distribution of doors and windows to extract structural information of the building facades, based on structural information automatically build the shape grammar, syntax and then use the compiler to generate the shape of the building facade model.(6) integration of airborne LIDAR data to extract the roof height and contour information on the missing portion of a building facade reasoning, combined with terrestrial LIDAR data to extract more detailed outline for the building up in the face refinement. To achieve better integration of two kinds of data to construct the purpose of building models. (7) combined with two representative of airborne and terrestrial LIDAR data, the proposed method and the theory was, to extract out of a more sophisticated model of the building facade, and the footprint of the buildings are refined, verified that the the feasibility and effectiveness of the proposed theory.This paper did't use any optical images, but reply only on the LIDAR data to extract geometric information,combining with a top down way to do the reconstruction of buildings. The proposed method approached a new thought about updating the large scale 3D urban scene. Now the result with detailed geometric information has a great simulating space for that no texturing mapping is done yet.