THE POINT CLOUD SEMANTIC SEGMENTATION METHOD FOR THE DOUGONG OF MING AND QING DYNASTY OFFICIAL-STYLE ARCHITECTURES

Abstract. To solve the problem that Dougong has various shapes and complex structures, the corresponding solutions are proposed in this paper. Our proposed method mainly consist of two parts. At first, the surface primitives were segmented using the machine learning (Random Forest). In this stage, the features including the curvature, normas and other features based on covariance matrix. Then, the knowledge from the construction rules were applied to label the segmented surface primitives into correct categories. The corresponding height constraint, concave-convex constraint, and symmetry constraint are proposed as the judgment conditions to mark the geometric elements belonging to the same dougong component and complete the point cloud segmentation of the dougong component. To verify the performance of our proposed method, the point cloud of a Qing-style single-arch flat-bodied Dougong was tested. The experimental results show that the classification accuracy of point cloud is 96.0%.