  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.
