LGNN: A Context-aware Line Segment Detector

• URL: http://arxiv.org/abs/2008.05892v2
• Date: Sat, 29 Aug 2020 03:43:06 GMT
• Title: LGNN: A Context-aware Line Segment Detector
• Authors: Quan Meng, Jiakai Zhang, Qiang Hu, Xuming He, Jingyi Yu
• Abstract summary: We present a novel real-time line segment detection scheme called Line Graph Neural Network (LGNN) 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. Compared with the state-of-the-art, LGNN achieves near real-time performance without compromising accuracy.
• Score: 53.424521592941936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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