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

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.