  Access to large and diverse computer-aided design (CAD) drawings is critical
for developing symbol spotting algorithms. In this paper, we present
FloorPlanCAD, a large-scale real-world CAD drawing dataset containing over
10,000 floor plans, ranging from residential to commercial buildings. CAD
drawings in the dataset are all represented as vector graphics, which enable us
to provide line-grained annotations of 30 object categories. Equipped by such
annotations, we introduce the task of panoptic symbol spotting, which requires
to spot not only instances of countable things, but also the semantic of
uncountable stuff. Aiming to solve this task, we propose a novel method by
combining Graph Convolutional Networks (GCNs) with Convolutional Neural
Networks (CNNs), which captures both non-Euclidean and Euclidean features and
can be trained end-to-end. The proposed CNN-GCN method achieved
state-of-the-art (SOTA) performance on the task of semantic symbol spotting,
and help us build a baseline network for the panoptic symbol spotting task. Our
contributions are three-fold: 1) to the best of our knowledge, the presented
CAD drawing dataset is the first of its kind; 2) the panoptic symbol spotting
task considers the spotting of both thing instances and stuff semantic as one
recognition problem; and 3) we presented a baseline solution to the panoptic
symbol spotting task based on a novel CNN-GCN method, which achieved SOTA
performance on semantic symbol spotting. We believe that these contributions
will boost research in related areas.
