Reamostragem adaptativa para simplificação de nuvens de pontos

Abstract

This paper presents a simple and efficient algorithm for point cloud simplification based on the local inclination of the surface sampled by the input set. The objective is to transform the original point cloud in a small as possible one, keeping the features and topology of the original surface. The proposed algorithm performs an adaptive resampling of the input set, removing unnecessary points to maintain a level of quality defined by the user in the final dataset. The process consists of a recursive partitioning in the input set using Principal Component Analysis (PCA). PCA is applied for defining the successive partitions, for obtaining the linear approximations (planes) for each partition, and for evaluating the quality of those approximations. Finally, the algorithm makes a simple choice of the points to represent the linear approximation of each partition. These points are the final dataset of the simplification process. For result evaluation, a distance metric between polygon meshes, based on Hausdorff distance, was defined, comparing the reconstructed surface using the original point clouds and the reconstructed surface usingthe filtered ones. The algorithm achieved compression rates up to 95% of the input dataset,while reducing the total execution time of reconstruction process, keeping the features and the topology of the original model. The quality of the reconstructed surface using the filtered point cloud is also attested by the distance metric.