Update license, pipeline tag, and improve model card structure

Hi! I'm Niels, part of the community science team at Hugging Face. I've opened this PR to improve the model card for RAP. Key changes include:
- Updated the license to **MIT** in the metadata to match the official GitHub repository.
- Updated the `pipeline_tag` to `other`.
- Added the full list of authors and a link to the paper page on Hugging Face.
- Included the formal BibTeX citation for researchers to easily cite your work.
- Cleaned up the Markdown for better structure and readability.

These updates help the community better discover, understand, and attribute your research!

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}
+}
+```