D-FRAME: Direction-Field-Based Wireframe Extraction for Complex CAD Models.

Extracting wireframes from CAD models represented by point cloud remains a significant challenge in computer graphics. This difficulty arises from two main factors: first, imperfections in the point cloud data, such as lack of orientation, noise, and sparsity; and second, the inherent complexity of geometric shapes, which often feature a high density of sharp edges in close proximity. In this paper, we propose D-FRAME, a multi-stage wireframe extraction framework that incorporates a novel direction field to improve edge detection quality and connectivity, a refinement strategy to address sparse or noisy edge points, and a final coarse-to-fine connection module to extract a robust wireframe. The direction field not only facilitates connectivity but also enhances the precision of extracted edges by mitigating the impact of misclassified points. By combining the Restricted Voronoi Diagram (RVD) with the extracted wireframes and the original point cloud, our approach also achieves highly faithful reconstruction of CAD model. Experiments conducted on synthetic and real-world scanned CAD datasets demonstrate that D-FRAME effectively manages noise, sparsity, and complex geometries, yielding high-fidelity wireframes. Code is available at https://github.com/yuanfeng-01/D-FRAME-test.