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--- |
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license: cc-by-4.0 |
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name: DIEPF 2026 – Depth–Image + Extrusion + (Robot) Pose Fusion Dataset |
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language: |
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- en |
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task_categories: |
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- robotics |
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- image-classification |
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- time-series-forecasting |
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tags: |
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- construction-robotics |
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- additive-manufacturing |
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- rgbd |
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- sensor-fusion |
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- kuka |
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size_categories: |
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- 1B<n<10B |
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--- |
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# DIEPF 2026 – Depth–Image + Extrusion + (Robot) Pose Fusion Dataset for Large-Scale Additive Manufacturing |
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This repository contains **multi-modal recordings** for construction-scale additive manufacturing experiments, combining **RGB-D imagery** with **robot kinematics and process signals**. The dataset is designed for **quality monitoring**, **defect detection**, and **dataset generation** for learning-based approaches in robotic extrusion processes. |
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**Hardware & setup** |
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- Robot: **KUKA KR210 R3100** with **KRC4** controller |
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- Camera: **Intel RealSense D405** (short-range RGB-D), **eye-in-hand** on robot flange, viewing the nozzle at a fixed angle |
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- Robot telemetry: streamed via **KUKA variables** (OpenShowVar / VAR proxy), including joint axes and additional process variables (e.g., extruder RPM and override) |
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--- |
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## What’s inside |
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The dataset is organized as **one `.tar` archive per recording session**. |
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## Recording archives (download sizes) |
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The dataset is provided as **one TAR archive per recording session** (stored via **Git LFS**). |
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**Total size (all sessions): ~7.9 GB** |
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| Session | Archive | Size | |
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|---:|---|---:| |
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| 001 | `260109_Recording_001.tar` | 82.2 MB | |
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| 002 | `260109_Recording_002.tar` | 84.1 MB | |
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| 003 | `260109_Recording_003.tar` | 1.59 GB | |
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| 004 | `260109_Recording_004.tar` | 656 MB | |
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| 005 | `260109_Recording_005.tar` | 596 MB | |
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| 006 | `260109_Recording_006.tar` | 467 MB | |
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| 007 | `260109_Recording_007.tar` | 700 MB | |
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| 008 | `260109_Recording_008.tar` | 2.05 GB | |
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| 009 | `260109_Recording_009.tar` | 2.23 GB | |
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> Archive size scales with recording duration and camera FPS. Larger sessions typically contain longer continuous runs. |
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## Data format notes |
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### Image rotation |
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The RGB-D camera is mounted eye-in-hand; images may be **rotated (e.g., 180°)** before saving to match a consistent orientation. Applied rotation should be documented in per-session metadata. |
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## Example: download and extract one session (Python) |
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```python |
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from huggingface_hub import hf_hub_download |
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import tarfile |
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from pathlib import Path |
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repo_id = "ICoM-RWTH/DIEPF_2026" |
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filename = "recordings/260109_Recording_001.tar" |
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tar_path = hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=filename) |
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out_dir = Path("extracted/260109_Recording_001") |
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out_dir.mkdir(parents=True, exist_ok=True) |
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with tarfile.open(tar_path, "r") as tar: |
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tar.extractall(out_dir) |
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print("Extracted to:", out_dir) |
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``` |
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### Add this (citation) |
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```markdown |
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## Citation |
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```bibtex |
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@dataset{diepf2026, |
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title = {DIEPF 2026: Depth--Image + Extrusion + Robot Pose Fusion Dataset for Large-Scale Additive Manufacturing}, |
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author = {Benz, Hendrik and Nguyen Trong, The Vinh}, |
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year = {2026}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/datasets/ICoM-RWTH/DIEPF_2026} |
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} |
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``` |
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