GAT-CADNet: Graph Attention Network for Panoptic Symbol Spotting in CAD Drawings

TL;DR

GAT-CADNet introduces a graph attention network that effectively detects and classifies symbols in CAD drawings by modeling the drawings as graphs and predicting subgraphs, achieving superior results over existing methods.

Contribution

The paper presents a novel graph attention network with spatial and edge encoding modules for panoptic symbol spotting in CAD drawings, formulated as subgraph detection.

Findings

Outperforms state-of-the-art methods significantly

Effective subgraph detection approach for symbol spotting

Robust performance demonstrated on public benchmarks

Abstract

Spotting graphical symbols from the computer-aided design (CAD) drawings is essential to many industrial applications. Different from raster images, CAD drawings are vector graphics consisting of geometric primitives such as segments, arcs, and circles. By treating each CAD drawing as a graph, we propose a novel graph attention network GAT-CADNet to solve the panoptic symbol spotting problem: vertex features derived from the GAT branch are mapped to semantic labels, while their attention scores are cascaded and mapped to instance prediction. Our key contributions are three-fold: 1) the instance symbol spotting task is formulated as a subgraph detection problem and solved by predicting the adjacency matrix; 2) a relative spatial encoding (RSE) module explicitly encodes the relative positional and geometric relation among vertices to enhance the vertex attention; 3) a cascaded edge encoding (CEE) module extracts vertex attentions from multiple stages of GAT and treats them as edge encoding to predict the adjacency matrix. The proposed GAT-CADNet is intuitive yet effective and manages to solve the panoptic symbol spotting problem in one consolidated network. Extensive experiments and ablation studies on the public benchmark show that our graph-based approach surpasses existing state-of-the-art methods by a large margin.