| license: mit | |
| pipeline_tag: other | |
| # PatchAlign3D: Local Feature Alignment for Dense 3D Shape Understanding | |
| PatchAlign3D is an encoder-only 3D model that produces language-aligned patch-level features directly from point clouds. It enables zero-shot 3D part segmentation with fast single-pass inference without requiring test-time multi-view rendering. | |
| - **Paper:** [PatchAlign3D: Local Feature Alignment for Dense 3D Shape understanding](https://huggingface.co/papers/2601.02457) | |
| - **Project Page:** [https://souhail-hadgi.github.io/patchalign3dsite](https://souhail-hadgi.github.io/patchalign3dsite) | |
| - **Repository:** [https://github.com/souhail-hadgi/PatchAlign3D](https://github.com/souhail-hadgi/PatchAlign3D) | |
| ## Sample Usage | |
| You can run inference on a single shape and save per-point predictions using the following command from the official repository: | |
| ```bash | |
| python patchalign3d/inference/infer.py \ | |
| --ckpt /path/to/stage2_last.pt \ | |
| --input /path/to/shape.npz \ | |
| --labels "seat,back,leg,arm" | |
| ``` | |
| ## Citation | |
| ```bibtex | |
| @misc{hadgi2026patchalign3dlocalfeaturealignment, | |
| title={PatchAlign3D: Local Feature Alignment for Dense 3D Shape understanding}, | |
| author={Souhail Hadgi and Bingchen Gong and Ramana Sundararaman and Emery Pierson and Lei Li and Peter Wonka and Maks Ovsjanikov}, | |
| year={2026}, | |
| eprint={2601.02457}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2601.02457}, | |
| } | |
| ``` |