| dc.description.abstract | LIDAR, Light Detection And Ranging, is an emerging technique for optical remote sensing recently. Laser scanned point cloud is a valuable data source for building reconstruction because it can recover detailed building façade structures like wall, roof, pillar and window etc. Since the point cloud data are discrete, it is difficult to acquire useful 3D information directly. Therefore, before the generation of building models, necessary pre-processing, such as triangulated irregular network (TIN) organization, segmentation, or feature extraction etc., must be carried out.
Modeling from point cloud data can achieve high level of detail. However, due to the limitation of data, there will be complex procedures to reconstruct a highly accurate model. This research aims to reconstruct building models conforming to OGC CityGML LOD3 standard from LIDAR point clouds. The proposed method is divided into three main parts: data registration, points partitioning and surface reconstruction. First, after acquiring point cloud data from airborne and ground-based LIDAR, they are merged to a single dataset using 7-parameter transformation. The merged point cloud data are then partitioned into several groups according to different conditions such as coplanarity etc. For each point group, a three-dimensional surface is constructed based on Least Squares Method. Finally all surfaces are merged to reconstruct 3D models, and high resolution images are used as façade texture. The accuracy of reconstructed models are evaluated with ground measurement of check points.
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