---
license: mit
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- randlanet
- segmentation
datasets:
- semantic3d
model-index:
- name: randlanet.semantic3d.open3d
  results:
  - task:
      type: point-cloud-segmentation
    dataset:
      name: Semantic3D
      type: semantic3d
    metrics:
    - name: mIoU
      type: mean_iou
      value: 70.97
    - name: OA
      type: accuracy
      value: 83.39
---

# Model card for randlanet.semantic3d.open3d

A RandLA-Net point cloud semantic segmentation model (random sampling with local feature aggregation). Trained on Semantic3D.

## Model Details

- **Model Type:** Point cloud semantic segmentation
- **Model Stats:**
  - Params (M): 5.0
  - Input channels: 6
  - Classes: 8
  - Features: 32
- **Dataset:** Semantic3D
- **Metrics:** mIoU 70.97, OA 83.39
- **Paper:** [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://arxiv.org/abs/1911.11236)
- **Converted from:** [isl-org/Open3D-ML](https://github.com/isl-org/Open3D-ML) (MIT)
- **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(
    "randlanet.semantic3d.open3d",
    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),
    "color": torch.rand(num_points, 3) * 255,
    "intensity": torch.rand(num_points, 1),
    "segment": torch.zeros(num_points, dtype=torch.long),
}
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():
    features = model.forward_features(data.get("x"), data["pos"], data["batch"])

model.reset_classifier(num_classes=0)
with torch.no_grad():
    features = model(data.get("x"), data["pos"], data["batch"])  # (N, 32)
```

## Citation

```bibtex
@inproceedings{hu2020randlanet,
  title   = {RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds},
  author  = {Qingyong Hu and Bo Yang and Linhai Xie and Stefano Rosa and Yulan Guo and Zhihua Wang and Niki Trigoni and Andrew Markham},
  booktitle = {CVPR},
  year    = {2020}
}

@article{hackel2017semantic3d,
  title   = {Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark},
  author  = {Timo Hackel and Nikolay Savinov and Lubor Ladicky and Jan D. Wegner and Konrad Schindler and Marc Pollefeys},
  journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  volume  = {IV-1/W1},
  year    = {2017}
}

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