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

TL;DR

This paper introduces FloorPlanCAD, a large-scale CAD dataset for panoptic symbol spotting, and proposes a CNN-GCN method that achieves state-of-the-art results in recognizing both object instances and semantic stuff in CAD drawings.

Contribution

The paper presents the first large-scale CAD dataset for symbol spotting, defines the panoptic symbol spotting task, and introduces a novel CNN-GCN approach that sets new performance benchmarks.

Findings

The dataset contains over 10,000 annotated CAD floor plans.

The CNN-GCN method outperforms previous approaches in semantic symbol spotting.

The approach effectively captures both Euclidean and non-Euclidean features.

Abstract

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.