metadata
license: mit
pipeline_tag: image-segmentation
library_name: pytorch
tags:
- point-cloud
- point-cloud-backbone
- graph-learning
- pytorch
authors:
- Yuanwen Yue
- Damien Robert
- Jianyuan Wang
- Sunghwan Hong
- Jan Dirk Wegner
- Christian Rupprecht
- Konrad Schindler
This repository contains model weights for LitePT: Lighter Yet Stronger Point Transformer, a lightweight, high-performance 3D point cloud architecture.
LitePT embodies the simple principle "convolutions for low-level geometry, attention for high-level relations" and strategically places only the required operations at each hierarchy level. LitePT is equipped with a novel, parameter-free 3D positional encoding, PointROPE. The resulting model achieves state-of-the-art performance while being significantly more efficient.
Paper & Resources
- Paper: LitePT: Lighter Yet Stronger Point Transformer
- Arxiv: https://arxiv.org/abs/2512.13689
- Project Page: https://litept.github.io/
- Codebase: https://github.com/prs-eth/LitePT
Models
We release the pretrained model weights for the benchmarks we reported in our paper.
Semantic segmentation
| Model | Params | Benchmark | val mIoU | Config | Checkpoint |
|---|---|---|---|---|---|
| LitePT-S | 12.7M | NuScenes | 82.2 | link | Download |
| LitePT-S | 12.7M | Waymo | 73.1 | link | Download |
| LitePT-S | 12.7M | ScanNet | 76.5 | link | Download |
| LitePT-S | 12.7M | Structured3D | 83.6 | link | Download |
| LitePT-B | 45.1M | Structured3D | 85.1 | link | Download |
| LitePT-L | 85.9M | Structured3D | 85.4 | link | Download |
Instance segmentation
| Model | Params | Benchmark | mAP25 | mAP50 | mAP | Config | Checkpoint |
|---|---|---|---|---|---|---|---|
| LitePT-S* | 16.0M | ScanNet | 78.5 | 64.9 | 41.7 | link | Download |
| LitePT-S* | 16.0M | ScanNet200 | 40.3 | 33.1 | 22.2 | link | Download |
Object detection
| Model | Params | Benchmark | mAPH | Config | Checkpoint |
|---|---|---|---|---|---|
| LitePT | 9.0M | Waymo | 70.7 | link | Download |
Citation
@article{yuelitept2025,
title={{LitePT: Lighter Yet Stronger Point Transformer}},
author={Yue, Yuanwen and Robert, Damien and Wang, Jianyuan and Hong, Sunghwan and Wegner, Jan Dirk and Rupprecht, Christian and Schindler, Konrad},
journal={arXiv preprint arXiv:2512.13689},
year={2025}
}