updated readme

README.md

@@ -1 +1,21 @@
-# lidar-labelling
+# lidar-labelling
+[![Linters&Tests](https://github.com/prime-slam/lidar-labelling/actions/workflows/ci.yml/badge.svg)](https://github.com/prime-slam/lidar-labelling/actions/workflows/ci.yml)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+lidar-labelling is a tool for automatic segmentation of raw lidar clouds based on image segmentation.
+
+The labelling algorithm consists of two stages.
+
+The first stage consists of preliminary marking - a naive transfer of segmentation from images (performed by the [SAM](https://github.com/facebookresearch/segment-anything) algorithm) to the cloud. Then multi-stage processing of the cloud is performed, which allows us to make the cloud more compact before the final labelling without losing points that are significant for labelling. The preprocessing stages include removing points whose corresponding pixels were not marked on a sequence of images, selection of points close to the sensors, removal of noise, cloud voxelization.
+
+The next stage is segmentation itself. The segmentation criterion in this work is the distance between points, which is calculated through the physical distance and the degree of similarity of the labelling of points on several images. Based on the distance matrix, the prepared cloud is segmented using the [GraphCut](https://ieeexplore.ieee.org/abstract/document/937505) algorithm.
+
+## Datasets
+This tool currently supports processing of [KITTI](https://www.cvlibs.net/datasets/kitti/eval_odometry.php) dataset clouds. Support for other datasets involves writing an implementation of an abstract dataset class.
+
+## Usage
+Please check `example.ipynb` with a example of cloud segmentation from the [KITTI](https://www.cvlibs.net/datasets/kitti/eval_odometry.php) dataset.
+
+## License
+This project is licensed under the Apache License — 
+see the [LICENSE](https://github.com/prime-slam/lidar-labelling/blob/main/LICENSE) file for details.