---
license: apache-2.0
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- pointmlp
- classification
datasets:
- modelnet40
model-index:
- name: pointmlp-base.modelnet40.xu-ma
  results:
  - task:
      type: point-cloud-classification
    dataset:
      name: ModelNet40
      type: modelnet40
    metrics:
    - name: OA
      type: accuracy
      value: 93.88
---

# Model card for pointmlp-base.modelnet40.xu-ma

A PointMLP point cloud classification model (residual MLP with geometric affine grouping). Trained on ModelNet40.

## Model Details

- **Model Type:** Point cloud classification
- **Model Stats:**
  - Params (M): 13.2
  - Input channels: 3
  - Classes: 40
  - Features: 1024
- **Dataset:** ModelNet40
- **Metrics:** OA 93.88 (reference 94.1)
- **Paper:** [Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework](https://arxiv.org/abs/2202.07123)
- **Converted from:** [ma-xu/pointMLP-pytorch](https://github.com/ma-xu/pointMLP-pytorch) (Apache-2.0)
- **Library:** [torch-pointcloud](https://github.com/arthurdjn/pytorch-pointcloud)

## Install

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

## Usage

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

model, info = tp.create_model(
    "pointmlp-base.modelnet40.xu-ma",
    task="classification",
    pretrained=True,
    return_info=True,
)
model = model.eval()

# synthetic sample with the keys a dataset provides
num_points = 8192
sample = {
    "pos": torch.randn(num_points, 3),
    "normal": torch.randn(num_points, 3),
}
data = info["transform"](sample)
data = collate([data])

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

## Feature extraction

```python
with torch.no_grad():
    embeddings = model.forward_features(data.get("x"), data["pos"], data["batch"])

model.reset_classifier(num_classes=0)
with torch.no_grad():
    embeddings = model(data.get("x"), data["pos"], data["batch"])  # (B, 1024)
```

## Citation

```bibtex
@inproceedings{ma2022pointmlp,
  title   = {Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework},
  author  = {Xu Ma and Can Qin and Haoxuan You and Haoxi Ran and Yun Fu},
  booktitle = {ICLR},
  year    = {2022}
}

@inproceedings{wu2015modelnet,
  title   = {3D ShapeNets: A Deep Representation for Volumetric Shapes},
  author  = {Zhirong Wu and Shuran Song and Aditya Khosla and Fisher Yu and Linguang Zhang and Xiaoou Tang and Jianxiong Xiao},
  booktitle = {CVPR},
  year    = {2015}
}

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