The invention relates to a simplification method used for
point cloud data of large-scene high-precision three-dimension
laser measurement. The method can support fast simplification of the
point cloud data of large-scene high-precision three-dimension
laser scanning, and maintain key features in the
point cloud data at the same time, and belongs to the field of three-dimension modeling technology. According to the method, a uniform grid method is adopted to carry out spatial uniform division on scattered point cloud, a grid index corresponding to the point
cloud data is established, and spatial locations are utilized to fast find K neighborhoods of data points; point cloud feature points are extracted according to projection residual-values, surface variation values are utilized to carry out region division on the point
cloud data; and region division and the surface variation values of the data points are utilized to simplify the original point cloud, and simplified point
cloud data is finally obtained. According to the method of the invention, the data, of which a data amount is more than 100 million points, of high-precision scanning can be fast simplified, the execution speed is fast, key feature points in a scene can be maintained while the
data capacity is effectively reduced, and carrying out latter three-dimension modeling and other works is facilitated.