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README.md

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-INSTALL by conda env setup
+# Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration
+
+## Introduction
+
+This repository contains the implementation of our Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration. Our model is designed to process and align 3D point cloud data from various datasets, including 3DMatch and KITTI.
+[Read the full paper here](https://eccv.ecva.net/virtual/2024/poster/944)
+
+## Environment Setup
+
+To set up the environment for this project, we use Conda. Follow these steps:
+
+1. Make sure you have Conda installed. If not, you can download it from [here](https://docs.conda.io/en/latest/miniconda.html).
+
+2. Clone this repository, All required packages are specified in the `environment.yml` file. 
+
+$conda env create -f environment.yml
+$conda activate egnn-test
+
+## Data
+
+To run this project, you'll need to download the following datasets:
+
+- [3DMatch](https://drive.google.com/file/d/1wr21qFPvgoDWsBnMafew7h-vZfP242Gw/view?usp=drive_link)
+- [KITTI](https://drive.google.com/file/d/17u2AWfPIMbgCQUVtXYelgacv_Cyeh6EM/view?usp=sharing)
+
+## Data Processing
+
+For self-processing of data, we provide scripts in the `datasets` folder:
+
+- `3DMatch.py`: For processing 3DMatch dataset
+- `KITTI.py`: For processing KITTI dataset
+
+## Training
+
+To train the EGNN model, run the following script `train_egnn.py` in the `src` folder:
+$python src/train_egnn.py
+
+## Evaluation
+
+For evaluation, use the `evaluation.py` script located in the `tools` folder:
+$python tools/evaluation.py
+
+## Citation
+
+If you find our work useful in your research, please consider citing:
+
+```bibtex
+@article{kangequi,
+  title={Equi-GSPR: Equivariant SE (3) Graph Network Model for Sparse Point Cloud Registration},
+  author={Kang, Xueyang and Luan, Zhaoliang and Khoshelham, Kourosh and Wang, Bing}
+}