Point cloud simplification algorithm based on the feature of adaptive curvature entropy

Abstract

Considering the diversity of point cloud features, the simplification effects of traditional point cloud simplification algorithms (such as curvature, random and isometry simplification algorithms) are poor. To overcome these drawbacks, a point cloud simplification method based on adaptive curvature entropy is proposed. Points with large curvatures are extracted to construct the initial point cloud boundary by defining a given proportion. The point cloud is clustered using the dichotomy clustering method. Subsequently, a preliminary simplification based on an adaptive random algorithm is performed for each clustered point cloud to reduce the point cloud capacity. The curvature entropy of each clustered point cloud is calculated to remove redundant points and preserve feature points so that the simplified point cloud is eventually obtained. The extracted initial point cloud boundary and simplified point cloud constitute the final simplified result. The classic Stanford rabbit model is introduced to verify the effect of the proposed approach. Experimental results show that the proposed algorithm can effectively reflect the details of the point cloud despite a simplification proportion of up to 90%. Compared to traditional curvature simplification algorithms, the proposed method has the lowest deviation and highest accuracy at the same simplicity level, as numerous feature points are preserved, which facilitates the point cloud processing.