EDWG: Efficient Edge Detection and Wireframe Generation From Point Clouds

Yike Xu, Jianwei Guo, Li Cao, Xiaoping Liu

Abstract: Generating 3-D wireframe from point cloud has been widely applied in 3-D measurement. The primary challenge lies in accurately detecting edge points and constructing complete wireframe structures. To address this, edge detection and wireframe generation (EDWG) is proposed to efficiently generate wireframe from point clouds. The approach begins with a deep learning-based edge point detection network, incorporating local point self-attention (LPSA) and a multilevel feature fusion (MLFF) module to enhance edge perception. These innovative mechanisms can effectively capture edge information in complex environments, thus laying the foundation for wireframe generation. Following edge point detection, a four-step process is employed, which includes wireframe initialization, outlier removal, edge point reconnection, and spline fitting to construct the wireframe structure. Through extensive experiments on several challenging publicly datasets and measurement point clouds, EDWG achieves 98.8% accuracy in edge detection and reduces the Chamfer distance (CD) error of the wireframe to 0.012. The wireframe generated by EDWG can be directly applied to geometric tasks such as surface reconstruction.