README.md

---
license: cc-by-nc-4.0
pretty_name: iARCS Synthetic Indoor Scenes
size_categories:
- 10K<n<100K
tags:
- 3d
- indoor-scene-synthesis
- layout-generation
- 3d-front
- synthetic-data
- embodied-ai
configs:
- config_name: bedroom
  data_files: data/bedroom/scenes.parquet
- config_name: livingroom
  data_files: data/livingroom/scenes.parquet
- config_name: diningroom
  data_files: data/diningroom/scenes.parquet
- config_name: floor_plans_bedroom
  data_files: data/bedroom/floor_plans.parquet
- config_name: floor_plans_livingroom
  data_files: data/livingroom/floor_plans.parquet
- config_name: floor_plans_diningroom
  data_files: data/diningroom/floor_plans.parquet
---

# iARCS Synthetic Indoor Scenes

12,000 3D indoor scene layouts generated with **iARCS**: 4,000 bedrooms, 4,000 living rooms and 4,000 dining rooms.

- Paper: [iARCS: Iterative Agentic RL for Controllable 3D Scene Generation](https://arxiv.org/abs/2608.06161)
- Project page: https://saugat2002.github.io/iarcs/
- Code: https://github.com/thenaivekid/iARCS

## Contents

| Room | Scenes | Floor plans | Mean objects / scene |
|---|---|---|---|
| Bedroom | 4,000 | 162 | 4.92 |
| Living room | 4,000 | 192 | 9.10 |
| Dining room | 4,000 | 177 | 13.66 |

Floor plans come from the 3D-FRONT test split used by MiDiffusion. Each floor plan is reused for several generated scenes.

## Files

```
data/<room>/scenes.parquet       one row per scene
data/<room>/floor_plans.parquet  one row per floor plan
json/<room>/scenes.jsonl         same scenes as JSON Lines
json/<room>/floor_plans.json     same floor plans as JSON
json/<room>/categories.json      object category list
raw/<room>/results.pkl           original MiDiffusion output (needs the iARCS / MiDiffusion code to load)
raw/<room>/config.yaml           generation config
```

## Fields

**scenes**

| Field | Description |
|---|---|
| `scene_index` | 0–3999 |
| `room_type` | `bedroom`, `livingroom` or `diningroom` |
| `floor_plan_id` | 3D-FRONT room id; join with `floor_plans` |
| `num_objects` | number of objects |
| `objects` | list of objects (below) |

**objects**

| Field | Description |
|---|---|
| `category` | object class, e.g. `double_bed` |
| `translation` | object centre `[x, y, z]` in metres; Y is up |
| `half_extents` | half the bounding-box size `[x, y, z]` in metres |
| `angle` | rotation around the Y axis, radians |
| `jid` | 3D-FUTURE model id, retrieved as the same-category model closest in size |

**floor_plans**

| Field | Description |
|---|---|
| `floor_plan_id` | 3D-FRONT room id |
| `vertices` | floor mesh vertices, centred on `centroid` (same frame as object translations) |
| `faces` | floor mesh triangles (vertex indices) |
| `centroid` | floor-plan centroid in the original 3D-FRONT frame |

## Usage

```python
from datasets import load_dataset

scenes = load_dataset("Saugat20021/iARCS", "bedroom", split="train")
plans = load_dataset("Saugat20021/iARCS", "floor_plans_bedroom", split="train")

s = scenes[0]
print(s["floor_plan_id"], s["num_objects"])
for o in s["objects"]:
    print(o["category"], o["translation"], o["jid"])
```

To rebuild textured 3D scenes, place each 3D-FUTURE model `jid` at `translation`, rotate it by `angle` around Y, and scale it to `half_extents`. 3D-FRONT and 3D-FUTURE must be obtained separately under their own licenses. No CAD assets are included here.

## Notes

- A few living-room (4) and dining-room (2) scenes have no objects.
- The data is synthetic and contains no personal information. It inherits the coverage and furnishing style of 3D-FRONT.

## License

CC BY-NC 4.0. The layouts come from a model trained on 3D-FRONT and refer to 3D-FUTURE assets, so the non-commercial research terms of those datasets also apply.

## Citation

```bibtex
@article{adhikari2026iarcs,
  title   = {iARCS: Iterative Agentic RL for Controllable 3D Scene Generation},
  author  = {Adhikari, Saugat and Neupane, Ashok Prasad and Paudel, Pramish
             and Chhatkuli, Ajad and Paudel, Danda Pani},
  journal = {arXiv preprint arXiv:2608.06161},
  year    = {2026}
}
```