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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: 90.34
- name: OA
type: accuracy
value: 98.56
---
# 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 90.34, OA 98.56
- **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},
}
```
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