---
license: cc-by-4.0
task_categories:
  - object-detection
  - keypoint-detection
  - image-segmentation
tags:
  - synthetic
  - SEM
  - scanning-electron-microscopy
  - materials-science
  - ZIF-8
  - metal-organic-framework
  - 3d-keypoint
  - instance-segmentation
size_categories:
  - 10K<n<100K
pretty_name: SynZIF-8
---

# SynZIF-8: A Synthetic SEM Dataset and Benchmark for 3D Sub-Micron Crystal Perception

## Dataset Summary

SynZIF-8 is a synthetic Scanning Electron Microscope (SEM) dataset of 
rhombic dodecahedron crystals modeled after ZIF-8 metal-organic frameworks. 
The dataset contains **20,000 images** with **455K+ instance annotations** 
and zero human annotation cost, generated by chaining ControlNet and 
LoRA-stylized diffusion models with deterministic 3D rendering.

## Code Repository

The evaluation code, training scripts, and baseline implementations referenced
in the SynZIF-8 paper (NeurIPS 2026) are released at:

**🔗 https://github.com/synzif8/synzif8**

This includes:
- 3D rendering pipeline (rhombic dodecahedron geometry, Gaussian clustering,
  orthographic projection, per-instance annotation extraction)
- SEM stylization (Stable Diffusion 1.5 + LoRA + ControlNet + mask-guided
  contrast calibration)
- Mask R-CNN front-end detector (seg_exp07)
- Nine baseline implementations across three families:
  - 3D keypoint regression: FFB6D, REDE, Uni6D
  - Geometry-aware 6D pose: SC6D, GDR-Net, HccePose
  - Foundation-style 6D pose: MegaPose, GigaPose, FoundationPose
- Evaluation suite: edge-length RRMSE, MPJPE with per-instance z-offset
  correction
- FastAPI annotation tool + chemistry expert annotation notebooks

## Trained Baseline Checkpoints

Pre-trained weights for all nine baseline models reported in the paper (Sec. 4)
are provided under `checkpoints/`:

| File | Model | Category | Size |
|------|-------|----------|------|
| `checkpoints/ffb6d.pt` | FFB6D | (B) 3D Keypoint Regression | 141 MB |
| `checkpoints/rede.pt` | REDE | (B) 3D Keypoint Regression | 146 MB |
| `checkpoints/uni6d.pt` | Uni6D | (B) 3D Keypoint Regression | 606 MB |
| `checkpoints/sc6d.pt` | SC6D | (A) Geometry-Aware 6D Pose | 146 MB |
| `checkpoints/gdrnet.pt` | GDR-Net | (A) Geometry-Aware 6D Pose | 155 MB |
| `checkpoints/hccepose.pt` | HccePose | (A) Geometry-Aware 6D Pose | 112 MB |
| `checkpoints/megapose.pt` | MegaPose | (C) Foundation-Style 6D Pose | 120 MB |
| `checkpoints/gigapose.pt` | GigaPose | (C) Foundation-Style 6D Pose | 583 MB |
| `checkpoints/foundationpose.pt` | FoundationPose | (C) Foundation-Style 6D Pose | 17 MB |

Each file is a PyTorch `state_dict` (pure model weights, no metadata).
Load with `torch.load("checkpoints/<model>.pt", map_location="cpu")`.

## Dataset Structure

The dataset is provided as a single tar archive (`synzif8_full.tar`) 
containing the `dataset_v6/` directory.

### Per-image files

For each rendered scene `render_{ID}`, the following files are provided:

| File | Description |
|------|-------------|
| `render_{ID}.png` | Original rendered image (rhombic dodecahedron crystals) |
| `render_{ID}_styled.png` | SEM-stylized image (via ControlNet + LoRA) |
| `render_{ID}_edge.png` | Visible edge map |
| `render_{ID}_labeled.png` | Labeled visualization with instance IDs |
| `render_{ID}_mask.png` | Combined instance mask |
| `render_{ID}_masks/` | Per-instance amodal and visible masks |
| `render_{ID}_metadata.json` | 14 vertex coordinates, edge lengths, visibility, per-instance annotations |

## Splits

| Split | # Images |
|-------|---------:|
| Train | 16,000 |
| Validation | 2,000 |
| Test | 2,000 |

Split is fixed (seed=42) and shared across all benchmark experiments.

## Tasks Supported

- **Instance Segmentation** (amodal + visible masks)
- **3D Keypoint Detection** (14 vertices per crystal)
- **6D Pose Estimation**
- **Edge Length Measurement** (for downstream CO₂ diffusivity computation)

## Intended Use

SynZIF-8 is designed as both training data and an evaluation testbed for:
1. Models tackling SEM image analysis of crystalline particles
2. Benchmarking automated morphological measurement (edge length) against 
   human expert annotation
3. Evaluating 3D keypoint detection and 6D pose estimation in 
   sub-micron-scale microscopy domains

## License

CC-BY-4.0

## Limitations

- Synthetic-to-real domain gap may persist despite SEM stylization
- Models exclusively cover ZIF-8-like rhombic dodecahedron geometry
- A 2-pixel resolution discrepancy exists between rendered (686px) and 
  SEM-styled (688px) images

## Citation

To be added upon publication.