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litept-base.structured3d.prs-eth

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torch-pointcloud
point-cloud
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litept-base.structured3d.prs-eth
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---
license: mit
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- litept
- segmentation
datasets:
- structured3d
---

# Model card for litept-base.structured3d.prs-eth

A LitePT point cloud semantic segmentation model (convolutions in the early stages, attention with 3D rotary embedding in the late ones). Trained on Structured3D.

## Model Details

- **Model Type:** Point cloud semantic segmentation
- **Model Stats:**
  - Params (M): 45.1
  - Input channels: 6
  - Classes: 25
  - Features: 72
- **Dataset:** Structured3D
- **Paper:** [LitePT: Lighter Yet Stronger Point Transformer](https://arxiv.org/abs/2512.13689)
- **Converted from:** [prs-eth/LitePT](https://github.com/prs-eth/LitePT) (MIT)
- **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/latest/installation/).

## Usage

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

model, info = tp.create_model(
    "litept-base.structured3d.prs-eth",
    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():
    logits = model(data.get("x"), data["pos_grid"], data["batch"])
```

## Feature extraction

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

model.reset_classifier(num_classes=0)
with torch.no_grad():
    features = model(data.get("x"), data["pos_grid"], data["batch"])  # (N, 72)
```

## Citation

```bibtex
@inproceedings{yue2026litept,
  title   = {LitePT: Lighter Yet Stronger Point Transformer},
  author  = {Yuanwen Yue and Damien Robert and Jianyuan Wang and Sunghwan Hong and Jan Dirk Wegner and Christian Rupprecht and Konrad Schindler},
  booktitle = {CVPR},
  year    = {2026}
}

@inproceedings{zheng2020structured3d,
  title   = {Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling},
  author  = {Jia Zheng and Junfei Zhang and Jing Li and Rui Tang and Shenghua Gao and Zihan Zhou},
  booktitle = {ECCV},
  year    = {2020}
}

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