---
license: cc-by-nc-4.0
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- sonata
- self-supervised
---

# Model card for sonata-base.pretrain.fair

A Sonata self-supervised pretraining model (self-distilled point representation encoder).

> **Non-commercial.** These weights are released by [facebookresearch/sonata](https://github.com/facebookresearch/sonata) under CC BY-NC 4.0 and may be used for research and evaluation only.

## Model Details

- **Model Type:** Self-supervised pretraining
- **Model Stats:**
  - Params (M): 108.5
  - Input channels: 9
- **Paper:** [Sonata: Self-Supervised Learning of Reliable Point Representations](https://arxiv.org/abs/2503.16429)
- **Converted from:** [facebookresearch/sonata](https://github.com/facebookresearch/sonata) (CC-BY-NC-4.0)
- **Library:** [torch-pointcloud](https://github.com/arthurdjn/pytorch-pointcloud)

## Install

```bash
pip install torch-pointcloud
```

This checkpoint also needs `spconv` and `flash-attn`, which need a build matching your torch and CUDA: see the [installation guide](https://pytorch-pointcloud.org/installation/).

## Usage

```python
import torch
import torch_pointcloud as tp
from torch_pointcloud.utils.data import collate

model, info = tp.create_model(
    "sonata-base.pretrain.fair",
    task="base",
    pretrained=True,
    return_info=True,
)
model = model.cuda().eval()  # GPU-only kernels

# synthetic sample with the keys a dataset provides
num_points = 8192
sample = {
    "pos": torch.randn(num_points, 3),
    "color": torch.rand(num_points, 3) * 255,
    "normal": torch.randn(num_points, 3),
    "segment": torch.zeros(num_points, dtype=torch.long),
    "instance": torch.zeros(num_points, dtype=torch.long),
}
data = info["transform"](sample)
data = collate([data])
data = {key: value.cuda() for key, value in data.items()}

with torch.no_grad():
    out = model(data.get("x"), data["pos_grid"], data["batch"], pos=data["pos"])
```

## Citation

```bibtex
@inproceedings{wu2025sonata,
  title   = {Sonata: Self-Supervised Learning of Reliable Point Representations},
  author  = {Xiaoyang Wu and Daniel DeTone and Duncan Frost and Tianwei Shen and Chris Xie and Nan Yang and Jakob Engel and Richard Newcombe and Hengshuang Zhao and Julian Straub},
  booktitle = {CVPR},
  year    = {2025}
}

@software{dujardin2026pytorchpointcloud,
  author  = {Arthur Dujardin},
  title   = {PyTorch PointCloud},
  year    = {2026},
  doi     = {10.5281/zenodo.22159632},
  url     = {https://github.com/arthurdjn/pytorch-pointcloud},
}
```