Landmarks serve as critical reference points for determining spatial orientations. Owing to the complexity and diversity of the shapes of landmark buildings, numerous fine visual details can hinder the clear identification of three-dimensional (3D) landmark models, posing a challenge for their automatic generation. To address this issue, we propose a method based on topological perception to simplify 3D landmark models, focusing on enhancing global perception features by exaggerating topology-related features. This method involves three key steps: voxelization, hole exaggeration and model generation. We evaluated the effectiveness of exaggeration and conducted a quantitative analysis of its degree of application in landmark buildings. The results demonstrate that topology-based exaggeration significantly improves the perception of 3D landmark models, and the degree of exaggeration is inversely correlated with the proportion of topology-related visual features in the models. Furthermore, a comparative analysis of four commonly used simplification algorithms shows that our method outperforms the other methods across five key evaluation metrics.

Abstract Community detection is one of the most popular researches in a variety of complex systems, ranging from biology to sociology. In recent years, there’s an increasing focus on the rapid development of more complicated networks, namely multilayer networks. Communities in a single-layer network are groups of nodes that are more strongly connected among themselves than the others, while in multilayer networks, a group of well-connected nodes are shared in multiple layers. Most traditional algorithms can rarely perform well on a multilayer network without modifications. Thus, in this paper, we offer overall comparisons of existing works and analyze several representative algorithms, providing a comprehensive understanding of community detection methods in multilayer networks. The comparison results indicate that the promoting of algorithm efficiency and the extending for general multilayer networks are also expected in the forthcoming studies.