Datasets:
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)
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} }