Adaptive simplification for point cloud based on hierarchical clustering and topological connectivity model

Laser-scanning measurement, which has become a prevalent and challenging research topic, has a significant advantage in massive and large-scale data sets acquisition. For the problems that universally exist in massive and high density point cloud sampling, such as low efficiency and bad adaptive curvature, the spatial geometry character of linear point cloud structure is investigated to produce an edge-pair derivative algorithm for line scanning point cloud. On this basis, topological connectivity model is established. To generate dense points in high-curvature areas and sparse points in planar regions efficiently, the local normal-vector variation is substituted for Gaussian curvature to determine the degree of recursive subdivision. Meanwhile, the computational method for the non-equal weighted factor of local normal-vector is presented to estimate the local normal-vector of any point in topological structure. For further subdivision, non-uniform subdivision model whose subdivision criterion is normal variance is built to achieve the subdivision for dense points in high-curvature areas. A relevant simplification system based on the algorithm is developed by using Visual Studio. Many cases are implemented to demonstrate the performance and validate the effectiveness of the method. The comparison with other point-based methods is also performed to illustrate the superiority of the method.