WireframeNet: A novel method for wireframe generation from point cloud

Li Cao, Yike Xu, Jianwei Guo, Xiaoping Liu

Abstract: Generating wireframe from point clouds is a challenging task. To make this process easier, we introduce WireframeNet, a deep neural network that transforms point clouds into wireframes. The network inputs a set of disordered points and outputs a complete wireframe structure. We use the insight of the medial axis transform to filter the original point cloud, then predict a set of edge points by learning the geometric transformation, and finally analyze the connectivity between the edge points to construct the complete wireframe structure. We train and evaluate publicly available wireframe datasets and compare the results quantitatively and qualitatively with traditional and other deep learning-based methods. Extensive experiments have demonstrated the robust and efficient performance of our proposed WireframeNet for the task of wireframe structure extraction from point clouds.