  We present a novel real-time line segment detection scheme called Line Graph
Neural Network (LGNN). Existing approaches require a computationally expensive
verification or postprocessing step. Our LGNN employs a deep convolutional
neural network (DCNN) for proposing line segment directly, with a graph neural
network (GNN) module for reasoning their connectivities. Specifically, LGNN
exploits a new quadruplet representation for each line segment where the GNN
module takes the predicted candidates as vertexes and constructs a sparse graph
to enforce structural context. Compared with the state-of-the-art, LGNN
achieves near real-time performance without compromising accuracy. LGNN further
enables time-sensitive 3D applications. When a 3D point cloud is accessible, we
present a multi-modal line segment classification technique for extracting a 3D
wireframe of the environment robustly and efficiently.
