An Algorithm of Combining Delaunay TIN Models and Region Growing for Buildings Extraction

Abstract

Abstract. LiDAR technology has been widely applied in remote sensing and computer vision. Aiming at drawback of inefficient filtering and then extracting methods, the algorithm of combining Delaunay TIN models and region growing is proposed for more efficient building extraction. At First, Delaunay TIN models were built on raw LiDAR points to get connection of discrete points. Based on the geometry properties of triangles which edge points are located, protrusions edge points were extracted. Then, the extracted edge points were assigned as seed points in region growing. It yielded a point set of protrusion based on triangle network connections. Finally, since the size of non-building points is usually much smaller than the building points and non-building point sets can be deleted by threshold. The algorithm extracted building points without filtering operation, the simulation results indicate that it can improve efficiency in building extraction and guarantee the accuracy in different scenarios.

Intact Planar Abstraction of Buildings via Global Normal Refinement from Noisy Oblique Photogrammetric Point Clouds

“…Region growing: Region growing-based [26] planar extraction methods generally consist of three major factors: (1) the selection of the starting seed primitives; (2) the criteria to extend the seed region; and (3) the criteria to terminate growing. First, the choice of seed is not limited to points, but can also be triangles in a surface mesh [27] or initial planar primitives [28]. The most intuitive method to place a seed is random selection; however, if the seed primitive is located in areas with high noise, the growth regions may deviate from the expected regions.…”