#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the <a href="https://docs.voxel51.com/integrations/huggingface.html#loading-datasets-from-the-hub" target="_blank">Hugging Face integration docs</a>.

<a href="https://huggingface.co/datasets/Voxel51/FloorPlanCAD" target="_blank">![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)</a>

# Dataset Card for FloorPlanCAD (test split)

![image](https://huggingface.co/datasets/Voxel51/FloorPlanCAD/resolve/main/floorplancad.gif)

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 5308 samples.

## Installation

If you haven’t already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/FloorPlanCAD")

# Launch the App
session = fo.launch_app(dataset)
```

## Dataset Details

### Dataset Description

FloorPlanCAD is a large-scale real-world CAD drawing dataset containing over 15,000 annotated floor plans for panoptic symbol spotting in architectural drawings. The dataset provides line-grained vector annotations for 30 object categories across residential and commercial buildings.

**Key Features:**

- **Format**: Vector graphics (SVG) with corresponding PNG rasterizations
- **Scale**: 15,663 CAD drawings (originally 10,094 in v1, updated to 15,663)
- **Categories**: 30 classes total
  - 28 “thing” classes (countable instances): doors, windows, furniture, appliances, equipment
  - 2 “stuff” classes (semantic regions): wall, parking
- **Annotation Type**: Line-grained primitive-level annotations with semantic and instance labels
- **Original Split**: 6,382 training / 3,712 testing drawings
- **Privacy Protected**: Cropped into 20m × 20m blocks, 50% retention rate, sensitive text removed

* **Curated by**: Zhiwen Fan†, Lingjie Zhu†, Honghua Li, Xiaohao Chen, Siyu Zhu, Ping Tan (Alibaba A.I. Labs & Simon Fraser University, †Equal contribution)
* **Funded by**: Alibaba A.I. Labs
* **Language(s)**: Not applicable (architectural vector graphics)
* **License**: Creative Commons Attribution-NonCommercial 4.0 License
* **Project shutdown notice**: As of January 2023, the project was shut down and most participants left the company

### Dataset Sources

* **Repository**: https://floorplancad.github.io/ (Note: Project shut down in 2022)
* **Paper**: Fan et al. “FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting” (ICCV 2021)

## Uses

### Direct Use

This dataset is designed for:

- **Panoptic symbol spotting**: Detecting both countable object instances and semantic regions in architectural drawings
- **Instance segmentation**: Identifying individual furniture, fixtures, and building elements
- **Semantic segmentation**: Recognizing structural elements like walls and parking areas
- **CAD drawing analysis**: Training models for automated floor plan understanding
- **Architecture/Engineering/Construction (AEC) applications**: Automated 3D modeling from 2D CAD drawings

### Out-of-Scope Use

- **Commercial applications**: Dataset is licensed for non-commercial use only
- **Privacy-sensitive reconstruction**: The dataset is intentionally cropped and anonymized; attempting to reconstruct original complete floor plans or identify building locations violates privacy protections
- **As-is architectural design**: The cropped 20m × 20m blocks are not complete floor plans suitable for construction

## Dataset Structure

The converted FiftyOne dataset contains the following structure:

```text
<Sample: {
    'id': '690a547c0420c654cb79d521',
    'media_type': 'image',
    'filepath': '../image_data/0000-0003.png',
    'tags': [],
    'metadata': <ImageMetadata: {
        'size_bytes': 7803,
        'mime_type': 'image/png',
        'width': 1000,
        'height': 1000,
        'num_channels': 4,
    }>,
    'created_at': datetime.datetime(2025, 11, 4, 19, 31, 8, 427000),
    'last_modified_at': datetime.datetime(2025, 11, 4, 19, 39, 58, 326000),
    'ground_truth': <Detections: {
        'detections': [
            <Detection: {
                'id': '690a547c0420c654cb79d520',
                'attributes': {},
                'tags': [],
                'label': 'wall',
                'bounding_box': [0.30975255, 0.0, 0.69024745, 0.7205705549999999],
                'mask': array([[255, 255, 255, ...,   0,   0,   0],
                       [255, 255, 255, ...,   0,   0,   0],
                       [255, 255, 255, ...,   0,   0,   0],
                       ...,
                       [255, 255, 255, ...,   0,   0,   0],
                       [255, 255, 255, ..., 255, 255, 255],
                       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),
                'mask_path': None,
                'confidence': None,
                'index': None,
            }>,
        ],
    }>,
}>
```

**Object Categories (30 total):**

*Doors (3):* single_door, double_door, sliding_door

*Windows (4):* window, bay_window, blind_window, opening_symbol

*Stairs (1):* stair

*Home Appliances (3):* gas_stove, refrigerator, washing_machine

*Furniture (11):* sofa, bed, chair, table, bedside_cupboard, tv_cabinet, half_height_cabinet, high_cabinet, wardrobe, sink, bath

*Equipment (6):* bath_tub, squat_toilet, urinal, toilet, elevator, escalator

*Stuff Classes (2):* wall, parking

**Note on class distribution:** Wall and parking together account for ~27% of all annotated primitives. Significant class imbalance exists across categories.

## Dataset Creation

### Curation Rationale

The FloorPlanCAD dataset was created to address critical limitations in existing symbol spotting research:

1. **Scale**: Previous datasets (SESYD with 1,000 synthetic plans, FPLAN-POLY with 42 plans) were too small for deep learning
2. **Real-world diversity**: Prior datasets lacked the symbol variation seen across different architectural firms and building types
3. **Vector graphics**: Maintaining CAD’s native vector format (rather than rasterization) preserves accuracy and enables graph-based methods
4. **Panoptic scope**: Traditional symbol spotting focused only on “thing” instances; this dataset includes “stuff” classes (walls, parking) for complete scene understanding

### Source Data

#### Data Collection and Processing

**Original Data Sources:**

- 100+ architectural projects from production environments
- Multiple partner companies and institutions
- Building types: residential towers, schools, hospitals, shopping malls, office buildings
- Geographic diversity: Projects from various regions (layer names include Chinese characters indicating Asian sources)

**Technical Processing:**

- Multi-layer SVG organization (dozens of layers per drawing)
- Layer-by-layer annotation to reduce clutter
- Scale handling: Entity lengths range from millimeters to tens of meters (5+ orders of magnitude)
- Coordinate systems: Metric units (meters) for real-world measurements

#### Who are the source data producers?

- **Primary producers**: Architects, engineers, and CAD designers from various companies creating production floor plans
- **Data providers**: Multiple partner companies and institutions in the AEC industry (anonymized for privacy)
- **Geographic origin**: Multinational (layer names suggest significant Asian representation)

### Annotations

#### Annotation Process

**From SVG to FiftyOne Annotations:**

The conversion from vector SVG to structured annotations involves several stages:

1. **SVG Primitive Parsing** (using `svgpathtools`):
   ```python
   # For each <path>, <circle>, <ellipse> element:
   - Extract semantic-id (class label 1-35)
   - Extract instance-id (unique instance number or -1 for stuff)
   - Parse geometry:
     * Paths: start point, end point, middle point via path.point(0.5)
     * Circles: center (cx, cy), radius (r)
     * Ellipses: center, radii (rx, ry)
   ```
2. **Coordinate Transformation**:
   - SVG coordinates scaled by 10x to match PNG dimensions
   - svg_x \* 10 → png_x
   - Maintains accurate spatial relationships
3. **Instance Grouping**:
   - Primitives grouped by (semantic_id, instance_id) tuple
   - Each unique tuple represents one object instance
   - “Stuff” classes have instance_id = -1 (no individual instances)
4. **Bounding Box Computation**:
   ```python
   # For each instance:
   - Collect all primitive endpoints and centers
   - Compute axis-aligned bounding box:
     x_min = min(all_x_coordinates)
     y_min = min(all_y_coordinates)
     width = x_max - x_min
     height = y_max - y_min
   - Normalize to [0, 1] by dividing by image dimensions
   ```
5. **Segmentation Mask Rendering**:
   ```python
   # For each instance:
   - Create blank mask (image_height × image_width)
   - Render each primitive with line_width=3 pixels:
     * Paths: cv2.line() or cv2.polylines()
     * Circles: cv2.circle()
     * Ellipses: cv2.ellipse()
   - Crop mask to bounding box region
   ```
6. **FiftyOne Detection Object Creation**:
   - Each instance becomes `fo.Detection()` with:
     * label: mapped class name (e.g., “wall”, “single_door”)
     * bounding_box: normalized [x, y, w, h]
     * mask: binary array (if include_masks=True)

#### Who are the annotators?

- **Number**: 11 specialist annotators
- **Time investment**: Over 1,000 hours total annotation effort
- **Expertise**: Domain specialists familiar with architectural CAD drawings
- **Quality control**: Layer-by-layer annotation methodology for accuracy
- **Employer**: Alibaba A.I. Labs (annotation team)

## Citation

### BibTeX

```bibtex
@InProceedings{Fan_2021_ICCV,
    author    = {Fan, Zhiwen and Zhu, Lingjie and Li, Honghua and Zhu, Siyu and Tan, Ping},
    title     = {FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {10128-10137}
}
```

### APA

Fan, Z., Zhu, L., Li, H., Zhu, S., & Tan, P. (2021). FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting. In *Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)* (pp. 10128-10137).