LGNN: A Context-aware Line Segment Detector

TL;DR

LGNN introduces a real-time, structure-aware line segment detection method using deep learning and graph reasoning, significantly improving efficiency while maintaining accuracy, and extends to 3D environment modeling.

Contribution

The paper proposes LGNN, a novel neural network architecture combining DCNN and GNN for direct line segment proposal and connectivity reasoning, enabling real-time performance.

Findings

LGNN achieves near real-time detection without accuracy loss.

It outperforms existing methods in efficiency and structural reasoning.

Enables robust 3D environment wireframe extraction.

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.