Efficient Roof Vertex Clustering for Wireframe Simplification Based on the Extended Multiclass Twin Support Vector Machine

This study introduces an efficient approach for clustering roof wireframe vertices within the realm of model simplification based on a multiclass twin support vector machine (TWSVM) framework. The proposed method first assigns a dynamic label to each point of the input point cloud, and it then iteratively identifies k cluster center 3-D lines by maintaining short distances between wireframe candidate vertices sharing the same corner. In addition, it ensures that these wireframe candidates from one corner are distanced from the wireframe vertices from the other corners in a drafting roof dataset. This study extends the multiclass TWSVM to tackle the clustering problem of roof wireframe vertices, thus facilitating model simplification. Remarkably, this problem can be solved using a straightforward and efficient iterative algorithm. The results demonstrate that our proposed method achieves more accurate clustering results on 20 out of 24 roof wireframe vertex datasets compared with other relevant methods. Furthermore, the proposed method can efficiently and accurately extract the majority of vertices from roof wireframes in real-world Building3D dataset.