A Novel Fast Method for Point-sampled Model Simplification

A novel fast simplification method for point-sampled statue model is proposed. Simplifying method for 3d model reconstruction is a hot topic in the field of 3D surface construction. But it is difficult as point cloud of many 3d models is very large, so its running time becomes very long. In this paper, a two-stage simplifying method is proposed. Firstly, a feature-preserved non-uniform simplification method for cloud points is presented, which simplifies the data set to remove the redundancy while keeping down the features of the model. Secondly, an affinity clustering simplifying method is used to classify the point cloud into a sharp point or a simple point. The advantage of Affinity Propagation clustering is passing messages among data points and fast speed of processing. Together with the re-sampling, it can dramatically reduce the duration of the process while keep a lower memory cost. Both theoretical analysis and experimental results show that after the simplification, the performance of the proposed method is efficient as well as the details of the surface are preserved well