EEI-NET: EDGE-ENHANCED INTERPOLATION NETWORK FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDING POINT CLOUDS

Abstract. In recent years, the conservation research of historical buildings and cultural relics has received a lot of attention from the state and the people, which not only provides a deeper understanding of their historical value and cultural significance, but also promotes the expansion of conservation research to the three-dimensional level. In this context, the semantic segmentation of historical building components is particularly important, which can provide basic support for various historical building applications, such as research and study of historical buildings, repair and protection, and 3D fine reconstruction, etc. However, most of the current methods for semantic segmentation of point clouds of historical buildings suffer from the problems of not being able to fully exploit the local neighborhood information of point clouds and poor edge segmentation. Therefore, we propose a new deep learning semantic segmentation-based approach, which we call EEI-Net. It is an end-to-end deep neural network in which we designed an edge enhancement interpolation (EEI) module and an edge interaction classifier (EIC). The edge enhancement interpolation module performs edge enhancement interpolation by fusing multi-layer features between the encoder and decoder. The edge interaction classifier enables the interaction of edge information through information transfer between individual nodes. EEI-Net incorporates contextual features and better preserves and enhances the edge information of the point cloud. We conduct experiments on the constructed historical architecture dataset, and the results show that the proposed EEI-Net has better performance.