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README.md

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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
+
+![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}
+}