Add model card and metadata

README.md

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----
-license: mit
----
+---
+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},
+}
+```