---
title: CubiCasa5K Dataset Overview
url: https://www.emergentmind.com/topics/cubicasa5k-dataset
type: topic
---

# CubiCasa5K Dataset Overview

The CubiCasa5K dataset is a large-scale resource for automatic floorplan image analysis, comprising 5,000 rasterized floorplan images sourced primarily from Finnish real estate marketing material. Each sample includes dense, polygon-based annotations spanning over 80 object categories, enabling both geometric and semantic parsing. The dataset was constructed to address the scarcity of publicly-available, representative, and meticulously annotated floorplan image collections, particularly for use in machine learning and computer vision research on building interiors. A comprehensive baseline is provided with an improved multi-task convolutional neural network (CNN) architecture for semantic segmentation and structural element extraction [1904.01920].

## 1. Dataset Structure and Composition

CubiCasa5K offers a diverse, style-aware corpus. From an initial pool of approximately 15,000 images, 5,000 were selected using explicit criteria on clarity, completeness (full single-floor layouts), and the visibility of all key architectural elements (walls, rooms, doors, windows, furniture).

- **Subsets by style:**
  - High-quality architectural: 3,732 images
  - High-quality black-and-white CAD-style: 992 images
  - Colorful, hand-drawn/marketing style: 276 images

- **Train/val/test partitioning:**
  - Training: 4,200 images
  - Validation: 400 images
  - Test: 400 images

Each split was sampled to preserve style and size variability. Only legible, fully scanned, single-floor images with all necessary architectural elements were retained.

## 2. Annotation Protocol and Object Taxonomy

Polygonal annotation is performed for every semantically meaningful object using SVG format, with a rigorous 2-stage quality assurance protocol: initial self-review by the annotator, then independent QA engineer verification.

- **Annotation workflow:**
  1. Draw wall polygons (distinguishing outer and inner boundaries)
  2. Draw room polygons (covering all enclosed cells)
  3. Place icons and mark opening polygons

- **Category distribution (major classes):**
  - **Rooms (12 baseline classes):** Background, Outdoor, Wall, Kitchen, Living Room, Bedroom, Bath, Hallway, Railing, Storage, Garage, Other Rooms
  - **Icon/opening (11 baseline classes):** Window, Door, Closet, Electrical Appliance, Toilet, Sink, Sauna Bench, Fire Place, Bathtub, Chimney, Empty

- **Objects per image (averages across 5,000 samples):**
  - Walls: ~29.4
  - Rooms: ~13.8
  - Icons: ~27.3

The overall taxonomy comprises approximately 83 fine-grained classes, with details available in the project's repository.

## 3. Dataset Statistics and Comparative Overview

CubiCasa5K demonstrates substantial diversity and scale relative to prior datasets. Image widths range from 50 to ~8,000 pixels (median: 1,500 px; modes: ~600 px and 2,000 px). The number of rooms per floorplan peaks at 8–12; walls at 20–30 segments; icons typically 15–30 per image.

**Comparative metrics with existing datasets:**

| Dataset                | #Images | Resolution Range | #Object Classes | #Rooms   |
|------------------------|---------|------------------|-----------------|----------|
| R-FP-500 [dodgeMVA’17] |   500   | 56–1,427         | N/A             | N/A      |
| CVC-FP [Heras ’15]     |   122   | 905–7,383        | 50              | 1,320    |
| Liu et al. ’17         |   815   | 96–1,920         | 27              | 7,466    |
| CubiCasa5K             | 5,000   | 50–8,000         | 83              | 68,877   |

- Bedrooms are the most frequent room type (~16% of all room polygons), followed by Kitchens, Living Rooms, and Bathrooms.
- Doors (~20%), Windows (~18%), Sinks, and Toilets are the most common icons.

## 4. Mathematical Formalization

Let $I \in \mathbb{R}^{H \times W \times 3}$ denote the input rasterized floorplan image. Ground-truth annotations are given by $G = \{ (P_i, c_i) \}_{i=1}^N$, where $P_i = (x_{i,1}, \ldots, x_{i,k_i})$ is a $k_i$-vertex polygon and $c_i \in \{1, \ldots, C\}$ is the object label.

The baseline network $f_\theta(I)$ produces:
- $S_{\text{rooms}} \in \mathbb{R}^{H \times W \times R}$: per-pixel scores for the $R$ room classes
- $S_{\text{icons}} \in \mathbb{R}^{H \times W \times K}$: per-pixel scores for $K$ icon classes
- $\{H_j\}_{j=1}^M$: $M$ heatmaps for wall junctions, icon corners, and opening endpoints

## 5. Baseline Multi-Task Convolutional Neural Network

The provided baseline utilizes a ResNet–152 backbone (ImageNet → MPII pose transfer), and an hourglass decoder (10 blocks with skip connections). The architecture emits two semantic-segmentation heads (rooms, icons) and 21 heatmap regression heads.

- **Training:**
  - Loss function follows Kendall et al. (2018) multi-task uncertainty:
    $$
    \mathcal{L}_{\text{tot}} = \mathcal{L}_H + \mathcal{L}_S
    $$
  - Heatmap regression loss with learned uncertainty $\sigma_i$:
    $$
    \mathcal{L}_H = \sum_{i=1}^M \left[ \frac{1}{2 \sigma_i^2} \| H_i^{gt} - H_i^\theta \|_2^2 + \log (1 + \sigma_i) \right]
    $$
  - Cross-entropy segmentation loss per task with uncertainty $\sigma_k$:
    $$
    \mathcal{L}_S = \sum_{k \in \{\text{rooms}, \text{icons}\}} \left[ \frac{1}{\sigma_k} \left(-\sum_p y_{k,p} \log S_{k,p}\right) + \log \sigma_k \right]
    $$

- **Optimization:** Adam (learning rate 1e-3; $\beta_1 = 0.9$, $\beta_2 = 0.999$), batch size 20, up to 400 epochs. Training uses data augmentation (random 90° rotations, color jitter, random crop/scale to 256×256, zero-padding).

- **Hardware:** Single NVIDIA Titan X; full training completes in approximately 3 hours.

## 6. Evaluation Outcomes

### 6.1. Benchmarking on Liu et al. ’17

The network demonstrates consistent improvements over Liu et al. ’17, achieving the following (with and without integer programming (IP) post-processing and test-time augmentation (TTA)):

| Method             | Junction acc/rec | Opening acc/rec | Icon acc/rec | Room acc/rec |
|--------------------|------------------|-----------------|--------------|--------------|
| Liu et al. ’17     | 70.7 / 95.1      | 67.9 / 91.4     | 22.3 / 77.4  | 80.9 / 78.5  |
| Liu et al. + IP    | 94.7 / 91.7      | 91.9 / 90.2     | 84.0 / 74.6  | 84.5 / 88.4  |
| Ours               | 82.4 / 92.0      | 82.3 / 93.3     | 34.6 / 88.3  | 90.0 / 87.6  |
| Ours + IP          | 94.1 / 89.6      | 93.2 / 92.6     | 92.9 / 87.7  | 91.7 / 90.8  |
| Ours (TTA) + IP    | 95.0 / 89.7      | 94.5 / 92.9     | 93.6 / 87.3  | 92.2 / 90.2  |

- The baseline outperforms Liu et al. on all metrics without post-processing. Addition of IP and TTA pushes accuracy and recall above 92% for all categories.

### 6.2. Semantic Segmentation Results on CubiCasa5K

The primary evaluation treats parsing as a pixel-wise semantic-segmentation task, reporting overall accuracy, mean class accuracy, and mean Intersection-over-Union (IoU):

| Task              | Overall Acc | Mean Acc | Mean IoU |
|-------------------|-------------|----------|----------|
| Rooms val         | 84.5%       | 72.3%    | 61.0%    |
| Rooms test        | 82.7%       | 69.8%    | 57.5%    |
| Rooms (Poly) test | 77.3%       | 61.6%    | 49.3%    |
| Icons val         | 97.8%       | 62.8%    | 56.5%    |
| Icons test        | 97.6%       | 61.5%    | 55.7%    |
| Icons (Poly) test | 96.7%       | 45.3%    | 41.6%    |

Post-processing to extract actual polygons ("Poly") introduces accuracy drops, primarily due to errors in junction detection. On the test set, mean room-IoU is ~57.5% and icon-IoU is ~55.7%.

## 7. Limitations and Prospects

Despite its scale, very rare architectural symbols (frequency < 1%) remain under-represented. Current post-processing relies on heuristics; errors, especially in junction localization, can propagate and undermine polygon extraction. Future work suggested by the authors includes integration of an explicit object-detection head for icons (as in Dodge et al. ’17), exploration of direct polygon regression approaches (cf. Acuna et al. ’18), and extension of both dataset and methodology to multi-floor or 3D structures such as stairs and elevators.

The data, SVG annotations, and baseline implementations are publicly available at https://github.com/CubiCasa/CubiCasa5k, providing a standardized foundation for further work in automatic floorplan parsing, structural scene understanding, and downstream AR/VR applications [1904.01920].

Source: https://www.emergentmind.com/topics/cubicasa5k-dataset