prs-eth/ForPT

This repository contains model weights for the paper Toward A Foundation Model for Forest Point Clouds.

ForPT pretrains a LitePT backbone on a large corpus of unlabeled airborne (ALS), UAV (ULS) and mobile (MLS) laser scans covering diverse forest ecosystems. It is evaluated on four forestry tasks: semantic segmentation, instance segmentation, tree species classification and tree age regression. Self-supervised pretraining accelerates convergence, improves performance when annotations are scarce, and transfers better than task-specific supervised pretraining.

Paper & Resources

• Arxiv: https://arxiv.org/abs/2609.24787
• Project Page: https://prs-eth.github.io/ForPT
• Codebase: https://github.com/prs-eth/ForPT

Models

We release the pretrained backbone and the downstream models for the benchmarks reported in our paper.

Pretrained backbone

Model	Params	Pretraining data	Config	Checkpoint
ForPT (LitePT-S)	12.4M	ForPT corpus	link	Download

Downstream tasks (100% labels)

Task	Benchmark	Model	Config	Checkpoint
Semantic segmentation	FOR-instanceV2	LitePT-scratch	link	Download
		ForPT-linear probing	link	Download
		ForPT-decoder probing	link	Download
		ForPT-finetune	link	Download
Instance segmentation	FOR-instanceV2	Coming soon	-	-
Species classification	FOR-species20K	LitePT-scratch	link	Download
		ForPT-linear probing	link	Download
		ForPT-finetune	link	Download
Age regression	FORage	LitePT-scratch	link	Download
		ForPT-linear probing	link	Download
		ForPT-finetune	link	Download

To download all checkpoints into the layout expected by the configs, run from the root of the ForPT codebase:

hf download prs-eth/ForPT --local-dir weights --exclude ".gitattributes" --exclude "README.md"

Citation

@article{yue2026forpt,
    title={Toward a foundation model for forest point clouds},
    author={Yue, Yuanwen and Puliti, Stefano and Robert, Damien and Topalo{\u{g}}lu, Atakan and Xiang, Binbin and Wielgosz, Maciej and Wegner, Jan Dirk and Astrup, Rasmus and Rupprecht, Christian and Schindler, Konrad},
    journal={arXiv preprint arXiv:2609.24787},
    year={2026}
}