Updated README.md

README.md

@@ -1,3 +1,41 @@
 ---
 license: apache-2.0
+tags:
+- point-cloud
+- point-cloud-backbone
+- semantic segmentation
+- pytorch
+- embedded systems
+authors:
+- Samir Abou Haidar
+- Alexandre Chariot
+- Mehdi Darouich
+- Cyril Joly
+- Jean-Emmanuel Deschaud
+Conference:
+- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)
 ---
+
+This repository contains model weights for **HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation**, a lightweight, high-performing, and embedded 3D point cloud architecture. 
+
+In HARP-NeXt, we first propose a novel point cloud pre-processing methodology that significantly reduces computational overhead. Then, we design the Conv-SE-NeXt feature extraction block to efficiently capture representations without deep layer stacking per network stage. We also employ a multi-scale range-point fusion backbone that leverages information at multiple abstraction levels to preserve essential geometric details, thereby enhancing accuracy.
+
+## Paper & Resources
+- **Paper:** [HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation](https://ieeexplore.ieee.org/document/11246557)
+- **Arxiv:** [https://arxiv.org/abs/2510.06876](https://arxiv.org/abs/2510.06876)
+- **Codebase:** [https://github.com/SamirAbouHaidar/HARP-NeXt](https://github.com/SamirAbouHaidar/HARP-NeXt)
+
+## Models
+We release the pretrained model weights for the nuScenes and SemanticKITTI benchmarks we reported in our paper.
+
+## Citation
+
+```
+@INPROCEEDINGS{abouhaidar2025harpnext,
+  author={Abou Haidar, Samir and Chariot, Alexandre and Darouich, Mehdi and Joly, Cyril and Deschaud, Jean-Emmanuel},
+  booktitle={2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
+  title={HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation}, 
+  year={2025},
+  pages={5946-5953},
+  doi={10.1109/IROS60139.2025.11246557}}
+```