prs-eth / LitePT

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

Instance segmentation

Object detection

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}
}