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

arXiv: https://arxiv.org/abs/2105.07147 · HTML: https://mlsanity.com/paper/2105.07147/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 44 references that are in the map.
- First posted on arXiv 15 May 2021.

## Builds on (most cited first, 26 of 26)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [UPSNet: A Unified Panoptic Segmentation Network](https://mlsanity.com/paper/1901.03784/) (2019)
- [Bidirectional Graph Reasoning Network for Panoptic Segmentation](https://mlsanity.com/paper/2004.06272/) (2020)
- [BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation](https://mlsanity.com/paper/2003.14031/) (2020)
- [Symbol Spotting on Digital Architectural Floor Plans Using a Deep Learning-based Framework](https://mlsanity.com/paper/2006.00684/) (2020)
- [Fast Sketch Segmentation and Labeling with Deep Learning](https://mlsanity.com/paper/1807.11847/) (2018)
- [SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks](https://mlsanity.com/paper/2003.00678/) (2020)

## Built on by (most cited first, 28 of 28)

- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [GAT-CADNet: Graph Attention Network for Panoptic Symbol Spotting in CAD Drawings](https://mlsanity.com/paper/2201.00625/) (2022)
- [Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again](https://mlsanity.com/paper/2210.08122/) (2022)
- [Symbol as Points: Panoptic Symbol Spotting via Point-based Representation](https://mlsanity.com/paper/2401.10556/) (2024)
- [PolyRoom: Room-aware Transformer for Floorplan Reconstruction](https://mlsanity.com/paper/2407.10439/) (2024)
- [PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs](https://mlsanity.com/paper/2308.05744/) (2023)
- [CEQuest: Benchmarking Large Language Models for Construction Estimation](https://mlsanity.com/paper/2508.16081/) (2025)
- [SymPoint Revolutionized: Boosting Panoptic Symbol Spotting with Layer Feature Enhancement](https://mlsanity.com/paper/2407.01928/) (2024)
- [WAFFLE: Multimodal Floorplan Understanding in the Wild](https://mlsanity.com/paper/2412.00955/) (2024)
- [ArchCAD-400K: A Large-Scale CAD drawings Dataset and New Baseline for Panoptic Symbol Spotting](https://mlsanity.com/paper/2503.22346/) (2025)
- [CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings](https://mlsanity.com/paper/2412.07377/) (2024)
- [HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding?](https://mlsanity.com/paper/2504.18406/) (2025)
- [Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings](https://mlsanity.com/paper/2505.23395/) (2025)
- [LLM-Guided Agentic Floor Plan Parsing for Accessible Indoor Navigation of Blind and Low-Vision People](https://mlsanity.com/paper/2604.23970/) (2026)
- [MuraNet: Multi-task Floor Plan Recognition with Relation Attention](https://mlsanity.com/paper/2309.00348/) (2023)
- [Pixel-Wise Symbol Spotting via Progressive Points Location for Parsing CAD Images](https://mlsanity.com/paper/2404.10985/) (2024)
- [VectorGraphNET: Graph Attention Networks for Accurate Segmentation of Complex Technical Drawings](https://mlsanity.com/paper/2410.01336/) (2024)
- [DoorDet: Semi-Automated Multi-Class Door Detection Dataset via Object Detection and Large Language Models](https://mlsanity.com/paper/2508.07714/) (2025)
- [Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models](https://mlsanity.com/paper/2509.17283/) (2025)
- [Recognizing and Reconstructing a Multi-Unit Floor Plan](https://mlsanity.com/paper/2408.01526/) (2024)
- [Text-Enhanced Panoptic Symbol Spotting in CAD Drawings](https://mlsanity.com/paper/2510.11091/) (2025)
- [Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans](https://mlsanity.com/paper/2606.10953/) (2026)
- [Text-Aided Multi-Modal Panoptic Symbol Spotting for CAD Floor Plan Drawings](https://mlsanity.com/paper/2607.12678/) (2026)
- [PolarSym: Polar Geometry-aware Attention for CAD Floorplan Parsing](https://mlsanity.com/paper/2608.11793/) (2026)
- [Handoff-H1: An Orchestrated Vision-Agent System for Material Quantity Takeoff from Construction Blueprints](https://mlsanity.com/paper/2608.15032/) (2026)
- [Training-Free Agentic Computer Vision for Structural Component Detection in 2D Structural Framing Plans](https://mlsanity.com/paper/2608.17237/) (2026)
- [BlueprintAgent: Constraint-Triggered Targeted Revisits for Simulation-Ready Generation from Scanned Structural Blueprints](https://mlsanity.com/paper/2609.07362/) (2026)
- [ARCH-B: Architectural Representation, Comprehension and Hierarchy Benchmark](https://mlsanity.com/paper/2609.34047/) (2026)

## Abstract (arXiv)

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.