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Access to large and diverse computer-aided design (CAD) drawings is criticalfor developing symbol spotting algorithms. In this paper, we presentFloorPlanCAD, a large-scale real-world CAD drawing dataset containing over10,000 floor plans, ranging from residential to commercial buildings. CADdrawings in the dataset are all represented as vector graphics, which enable usto provide line-grained annotations of 30 object categories. Equipped by suchannotations, we introduce the task of panoptic symbol spotting, which requiresto spot not only instances of countable things, but also the semantic ofuncountable stuff. Aiming to solve this task, we propose a novel method bycombining Graph Convolutional Networks (GCNs) with Convolutional NeuralNetworks (CNNs), which captures both non-Euclidean and Euclidean features andcan be trained end-to-end. The proposed CNN-GCN method achievedstate-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. Ourcontributions are three-fold: 1) to the best of our knowledge, the presentedCAD drawing dataset is the first of its kind; 2) the panoptic symbol spottingtask considers the spotting of both thing instances and stuff semantic as onerecognition problem; and 3) we presented a baseline solution to the panopticsymbol spotting task based on a novel CNN-GCN method, which achieved SOTAperformance on semantic symbol spotting. We believe that these contributionswill boost research in related areas.

FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting