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Spotting graphical symbols from the computer-aided design (CAD) drawings isessential to many industrial applications. Different from raster images, CADdrawings are vector graphics consisting of geometric primitives such assegments, arcs, and circles. By treating each CAD drawing as a graph, wepropose a novel graph attention network GAT-CADNet to solve the panoptic symbolspotting problem: vertex features derived from the GAT branch are mapped tosemantic labels, while their attention scores are cascaded and mapped toinstance prediction. Our key contributions are three-fold: 1) the instancesymbol spotting task is formulated as a subgraph detection problem and solvedby predicting the adjacency matrix; 2) a relative spatial encoding (RSE) moduleexplicitly encodes the relative positional and geometric relation amongvertices to enhance the vertex attention; 3) a cascaded edge encoding (CEE)module extracts vertex attentions from multiple stages of GAT and treats themas edge encoding to predict the adjacency matrix. The proposed GAT-CADNet isintuitive yet effective and manages to solve the panoptic symbol spottingproblem in one consolidated network. Extensive experiments and ablation studieson the public benchmark show that our graph-based approach surpasses existingstate-of-the-art methods by a large margin.

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