Abstract: Due to the inherent defects of laser sensors, the original point cloud of noncooperate targets is usually irregularly distributed, which brings great challenges to highquality 3D surface reconstruction of noncooperate targets. In this paper, we propose a local hierarchical clustering method based on global constraints to improve the consistency of noncooperative target point cloud distribution. Specifically, our method includes two main steps. The first one is the adaptive octreebased 3D spatial decomposition with global constraints and the second one is hierarchical clustering based on global constraints. The main purpose of the former one is to reduce the complexity of the algorithm, and the later one aims to convert the nonuniform point set to uniform one. Experiments are carried out on three noncooperative target models. The results of visualization and quantitative calculation verify the effectiveness of our method.

Key words: point cloud, noncooperative target, 3D reconstruction