Update license, pipeline tag, and improve model card structure (#1)

- Update license, pipeline tag, and improve model card structure (bef6307c143f1b0723ab0fdd2c8c827bd131ccc4)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,6 +1,6 @@
 ---
-license: apache-2.0
-pipeline_tag: robotics
+license: mit
+pipeline_tag: other
 tags:
 - point-cloud
 - 3d-vision
@@ -12,7 +12,7 @@ tags:
 # Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching
 
 [![Project Page](https://img.shields.io/badge/Project_Page-RAP-red)](https://register-any-point.github.io/) 
-[![arXiv](https://img.shields.io/badge/arXiv-2512.01850-blue?logo=arxiv&color=%23B31B1B)](https://arxiv.org/pdf/2512.01850) 
+[![arXiv](https://img.shields.io/badge/arXiv-2512.01850-blue?logo=arxiv&color=%23B31B1B)](https://huggingface.co/papers/2512.01850) 
 [![GitHub](https://img.shields.io/badge/GitHub-RAP-black?logo=github)](https://github.com/PRBonn/RAP)
 [![Demo](https://img.shields.io/badge/Gradio-RAP-red?logo=gradio)](https://f299fbbc3c4f12d152.gradio.live/)
 
@@ -20,5 +20,31 @@ tags:
 
 **Register Any Point (RAP)** is a single-stage multi-view point cloud registration model based on conditional flow matching generation in the Euclidean space. RAP model generalises to point clouds with diverse scales, sensors, view counts, and overlapping ratios. 
 
+## Paper Information
 
-For details, please check our [Github repository](https://github.com/PRBonn/RAP)
+- **Paper**: [Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching](https://huggingface.co/papers/2512.01850)
+- **Authors**: Yue Pan, Tao Sun, Liyuan Zhu, Lucas Nunes, Iro Armeni, Jens Behley, Cyrill Stachniss.
+- **Project Page**: [https://register-any-point.github.io/](https://register-any-point.github.io/)
+
+## Summary
+
+Point cloud registration aligns multiple unposed point clouds into a common reference frame and is a core step for 3D reconstruction and robot localization. RAP casts registration as conditional generation: a learned, continuous point-wise velocity field transports noisy points to a registered scene, from which the pose of each view is recovered. 
+
+Unlike prior methods that perform correspondence matching to estimate pairwise transformations and then optimize a pose graph for multi-view registration, RAP directly generates the registered point cloud, yielding both efficiency and point-level global consistency.
+
+## Installation and Usage
+
+For details on installation and running inference, please check the [official GitHub repository](https://github.com/PRBonn/RAP).
+
+## Citation
+
+If you use RAP in your research, please cite:
+
+```bibtex
+@article{pan2025arxiv,
+  title = {{Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching}},
+  author = {Pan, Yue and Sun, Tao and Zhu, Liyuan and Nunes, Lucas and Armeni, Iro and Behley, Jens and Stachniss, Cyrill},
+  journal = {arXiv preprint arXiv:2512.01850},
+  year    = {2025}
+}
+```