---
license: cc-by-nc-4.0
task_categories:
- image-segmentation
- object-detection
- depth-estimation
tags:
- computer-vision
- synthetic-data
- robotics
- autonomous-driving
- amr
- slam
- blender
- procedural-generation
pretty_name: DataFurnace AMR Synthetic Dataset
size_categories:
- n<1K
---

# 🏭 DataFurnace: AMR & SLAM Synthetic Evaluation Dataset

![DataFurnace Showcase](./assets/Warehouse_Zapping_Promo_0001_NORMAL.gif)
*Fully synchronized multi-pass rendering (RGB, Depth, Semantic Mask, and 3D Bounding Box).*

---

## 📌 Overview

**DataFurnace** is a procedurally generated synthetic dataset designed for evaluating and training:

- Autonomous Mobile Robots (AMR)  
- Automated Guided Vehicles (AGV)  
- SLAM / VIO / 3D Perception algorithms  
- Warehouse robotics navigation systems  

All ground truth is generated directly from the underlying 3D scene graph, ensuring **deterministic pixel-level and millimeter-level accuracy** across all modalities.  
**No generative AI is used**, eliminating copyright, privacy, and hallucination concerns.

---

## 🚀 Key Features

### 🔹 1. Perfectly Synchronized Multi-Pass Rendering
Each frame contains fully aligned multi-modal outputs:

- **RGB** — Physically lit color images  
- **Depth** — True Z-depth with LiDAR noise simulation
- **Semantic Mask** — Pixel-perfect class segmentation (Floor, Rack, Box, Pallet)
- **3D Bounding Box (JSON)** — 6DoF absolute coordinates and dimensions
- **Costmap** — 2D top-down occupancy grid (generated per scene/mode)

### 🔹 2. Extreme Hazard Simulation for Robustness Testing
To stress-test SLAM and perception systems, four lighting conditions are provided:

- **NORMAL** — Standard warehouse lighting
- **BROKEN** — Random darkened areas / flickering lights
- **GLARE** — Volumetric scattering, lens flare, white-out
- **EDGE_CASE** — Foreground blackout + background overexposure

---

## 📂 Dataset Structure

This repository contains curated sample sequences generated by the DataFurnace pipeline.

- **5 Unique Warehouse Environments**
- **Layout Variations** (Normal / Anomaly with Scattered Obstacles)
- **4 Lighting Conditions** per environment
- Sequential Frames per sequence (from robot cameras: Front/Back/Left/Right)

### Directory Layout (Example)
```text
dataset/
  └── Warehouse_Scene_0001/
      └── normal/
          └── NORMAL/
              ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_RGB.png
              ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_Depth.png
              ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_Mask.png
              ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_BBox.json
              └── ...
```

### 🧠 3D Bounding Box (JSON) Format
The dataset provides absolute 3D spatial data (6DoF), not just 2D projection. Camera poses and objects' Volumetric Centers are perfectly recorded.

JSON
{
    "frame": 1,
    "lighting_mode": "BROKEN",
    "camera_name": "Cam_Front",
    "camera_pose": {
        "location": {"x": 0.0, "y": -10.0, "z": 0.4},
        "rotation": {"x": 1.5708, "y": 0.0, "z": 0.0}
    },
    "objects": [
        {
            "class": "Box",
            "name": "ANOMALY_CardboardBox.248",
            "location": [-0.3007, 1.0295, 0.132],
            "rotation_euler": [0.0001, 0.0, -1.8494],
            "dimensions": [0.4266, 0.3861, 0.2639]
        }
    ]
}

### 📥 How to Use (Hugging Face Datasets)
Python
from datasets import load_dataset

# Load the dataset (Example usage)
ds = load_dataset("jp-cypress/DataFurnace-AMR")

### 🛠️ Verification Tools
Utility Python scripts are included in the scripts/ directory to help you visualize the ground truth accuracy without affecting the raw data.

Bash
# 1. Visualize 3D Bounding Boxes mathematically projected onto RGB images
python scripts/draw_bbox_overlay.py --input ./dataset/Warehouse_Scene_0001/normal/NORMAL

# 2. Generate a zapping GIF to easily review multi-modal alignment
python scripts/generate_promo_gif.py --input ./dataset/... --overlay ./output_bbox --out final.gif

### 📜 License
This dataset is released under CC BY-NC 4.0.

Commercial use is not permitted without explicit permission.

### 🌐 Citation
If you use DataFurnace in academic or industrial research, please cite this repository:

@dataset{datafurnace2026,
  author = {2.5D Asset Factory},
  title = {DataFurnace: AMR & SLAM Synthetic Evaluation Dataset},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/jp-cypress/DataFurnace-AMR}
}