| ---
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| license: cc-by-nc-4.0
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| task_categories:
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| - image-segmentation
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| - object-detection
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| - depth-estimation
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| tags:
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| - computer-vision
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| - synthetic-data
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| - robotics
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| - autonomous-driving
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| - amr
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| - slam
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| - blender
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| - procedural-generation
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| pretty_name: DataFurnace AMR Synthetic Dataset
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| size_categories:
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| - n<1K
|
| ---
|
|
|
| # 🏭 DataFurnace: AMR & SLAM Synthetic Evaluation Dataset
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|
|
| 
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| *Fully synchronized multi-pass rendering (RGB, Depth, Semantic Mask, and 3D Bounding Box).*
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|
|
| ---
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|
|
| ## 📌 Overview
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|
|
| **DataFurnace** is a procedurally generated synthetic dataset designed for evaluating and training:
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|
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| - Autonomous Mobile Robots (AMR)
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| - Automated Guided Vehicles (AGV)
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| - SLAM / VIO / 3D Perception algorithms
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| - Warehouse robotics navigation systems
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|
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| All ground truth is generated directly from the underlying 3D scene graph, ensuring **deterministic pixel-level and millimeter-level accuracy** across all modalities.
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| **No generative AI is used**, eliminating copyright, privacy, and hallucination concerns.
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|
|
| ---
|
|
|
| ## 🚀 Key Features
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|
|
| ### 🔹 1. Perfectly Synchronized Multi-Pass Rendering
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| Each frame contains fully aligned multi-modal outputs:
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|
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| - **RGB** — Physically lit color images
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| - **Depth** — True Z-depth with LiDAR noise simulation
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| - **Semantic Mask** — Pixel-perfect class segmentation (Floor, Rack, Box, Pallet)
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| - **3D Bounding Box (JSON)** — 6DoF absolute coordinates and dimensions
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| - **Costmap** — 2D top-down occupancy grid (generated per scene/mode)
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|
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| ### 🔹 2. Extreme Hazard Simulation for Robustness Testing
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| To stress-test SLAM and perception systems, four lighting conditions are provided:
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|
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| - **NORMAL** — Standard warehouse lighting
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| - **BROKEN** — Random darkened areas / flickering lights
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| - **GLARE** — Volumetric scattering, lens flare, white-out
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| - **EDGE_CASE** — Foreground blackout + background overexposure
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|
|
| ---
|
|
|
| ## 📂 Dataset Structure
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|
|
| This repository contains curated sample sequences generated by the DataFurnace pipeline.
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|
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| - **5 Unique Warehouse Environments**
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| - **Layout Variations** (Normal / Anomaly with Scattered Obstacles)
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| - **4 Lighting Conditions** per environment
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| - Sequential Frames per sequence (from robot cameras: Front/Back/Left/Right)
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|
|
| ### Directory Layout (Example)
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| ```text
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| dataset/
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| └── Warehouse_Scene_0001/
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| └── normal/
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| └── NORMAL/
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| ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_RGB.png
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| ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_Depth.png
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| ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_Mask.png
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| ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_BBox.json
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| └── ...
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| ```
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|
|
| ### 🧠 3D Bounding Box (JSON) Format
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| The dataset provides absolute 3D spatial data (6DoF), not just 2D projection. Camera poses and objects' Volumetric Centers are perfectly recorded.
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|
|
| JSON
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| {
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| "frame": 1,
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| "lighting_mode": "BROKEN",
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| "camera_name": "Cam_Front",
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| "camera_pose": {
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| "location": {"x": 0.0, "y": -10.0, "z": 0.4},
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| "rotation": {"x": 1.5708, "y": 0.0, "z": 0.0}
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| },
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| "objects": [
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| {
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| "class": "Box",
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| "name": "ANOMALY_CardboardBox.248",
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| "location": [-0.3007, 1.0295, 0.132],
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| "rotation_euler": [0.0001, 0.0, -1.8494],
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| "dimensions": [0.4266, 0.3861, 0.2639]
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| }
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| ]
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| }
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|
|
| ### 📥 How to Use (Hugging Face Datasets)
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| Python
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| from datasets import load_dataset
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|
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| # Load the dataset (Example usage)
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| ds = load_dataset("jp-cypress/DataFurnace-AMR")
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|
|
| ### 🛠️ Verification Tools
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| Utility Python scripts are included in the scripts/ directory to help you visualize the ground truth accuracy without affecting the raw data.
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|
|
| Bash
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| # 1. Visualize 3D Bounding Boxes mathematically projected onto RGB images
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| python scripts/draw_bbox_overlay.py --input ./dataset/Warehouse_Scene_0001/normal/NORMAL
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|
|
| # 2. Generate a zapping GIF to easily review multi-modal alignment
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| python scripts/generate_promo_gif.py --input ./dataset/... --overlay ./output_bbox --out final.gif
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|
|
| ### 📜 License
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| This dataset is released under CC BY-NC 4.0.
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|
|
| Commercial use is not permitted without explicit permission.
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|
|
| ### 🌐 Citation
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| If you use DataFurnace in academic or industrial research, please cite this repository:
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|
|
| @dataset{datafurnace2026,
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| author = {2.5D Asset Factory},
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| title = {DataFurnace: AMR & SLAM Synthetic Evaluation Dataset},
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| year = {2026},
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| publisher = {Hugging Face},
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| url = {https://huggingface.co/datasets/jp-cypress/DataFurnace-AMR}
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| } |
