---
license: bsd-2-clause
pipeline_tag: image-to-3d
base_model: yyfz233/Pi3
tags:
- lux3d
- rust
- candle
- safetensors
- canonical-weights
- 3d
- image-to-3d
- point-cloud
- pi3
---

# Lux3D Pi3 Canonical Weights

This repository contains the canonicalized `safetensors` export of the Pi3 checkpoint used by the Lux3D runtime.

## Model Details

- Model family: `pi3`
- Canonical file: `model.safetensors`
- Tensor count: `1210`
- Primary runtime target: Lux3D runtime
- Typical output: affine-invariant point cloud export (`.ply`)

## Included Files

- `model.safetensors`
- `resolved_config.json`
- `manifest.json`
- `checksums.json`

## Usage With Lux3D

The Lux3D CLI can validate and use the package once it is installed into your local model asset layout.

```powershell
cargo run -p lux3d-cli -- inspect --repo-root <runtime-root> pi3
cargo run -p lux3d-cli -- run --repo-root <runtime-root> pi3 --source <input-sequence> --output <output-file.ply>
```

## Provenance

- Upstream source model: [`yyfz233/Pi3`](https://huggingface.co/yyfz233/Pi3)
- Canonicalization flow: `tools/python_baseline/normalize_weights.py`

The exact source checksum and canonical checksum set are recorded in `manifest.json` and `checksums.json`.

## Intended Use

- image or video to 3D reconstruction with Lux3D
- point cloud export workflows based on the Pi3 contract
- reproducible runtime validation

## Limitations

- Verified Lux3D runtime inference currently assumes CUDA.
- This package is for the base `pi3` family, not `pi3x`.
- The repository contains canonicalized runtime artifacts, not the full upstream training or demo stack.

## License

- Upstream model page: [`yyfz233/Pi3`](https://huggingface.co/yyfz233/Pi3)
- License file in this repository: [LICENSE](LICENSE)

Review upstream terms before redistribution or commercial use.

## Citation

```bibtex
@article{wang2025pi,
  title={$\pi^3$: Scalable Permutation-Equivariant Visual Geometry Learning},
  author={Wang, Yifan and Zhou, Jianjun and Zhu, Haoyi and Chang, Wenzheng and Zhou, Yang and Li, Zizun and Chen, Junyi and Pang, Jiangmiao and Shen, Chunhua and He, Tong},
  journal={arXiv preprint arXiv:2507.13347},
  year={2025}
}
```