Research on Point Cloud Segmentation Method for Ancient Building Interior Scenes Based on Geometric Feature Multi-Scale Network

Aiming at addressing the problems of complex geometric features, high inter-class similarity, and insufficient single-scale information in semantic segmentation of point clouds for ancient building interior components, this paper takes the Ba Wang Academy of Shenyang Jianzhu University as the research object and proposes a Geometric Feature Multi-scale Network (GFMN). First, a point cloud dataset containing five types of components—windows, beams, walls, roofs, and columns—was collected and constructed using a FARO Focus3D X330 terrestrial laser scanner (FARO Technologies, Lake Mary, Florida, USA). Second, 46-dimensional handcrafted geometric descriptors were extracted for each discrete point from four aspects: basic point attributes, local geometric features, density and scale features, and multi-scale fusion. On this basis, features were grouped according to semantics and fed into independent encoding branches, where a gated adaptive fusion mechanism was employed to dynamically adjust the contribution of each branch, and optimization was performed in combination with a prototype classification head and a joint loss function. Experimental results show that the proposed method achieved an overall accuracy of 93.17% on the test set, significantly outperforming state-of-the-art methods such as PointNet, PointNet++, Point Transformer, and Point Cloud Transformer. This study provides an effective solution for high-precision semantic segmentation of ancient building interior components.