Point Cloud Simplification Based on an Affinity Propagation Clustering Algorithm

Point cloud simplification is an important step in reverse engineering and computer vision. Nowadays many researchers are directly working on point sets other than polygonal meshes, while some nasty problems still exist, such as time cost, memory cost and accuracy. This paper proposes a novel method for point cloud simplification by integrating both re-sampling and Affinity Propagation Clustering. The advantage of Affinity Propagation clustering is passing messages among data points and fast speed of processing. Together with the iterative re-sampling, it can dramatically reduce the duration of the process while keep a lower memory cost. The results of simulative experiments demonstrate that the proposed algorithm outperformed traditional clustering or re-sampling methods.