Datasets:
File size: 4,742 Bytes
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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
*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}
} |