songanz / 3d-mininet

Official Implementation in Pytorch and Tensorflow of 3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

Introduction

This repository contains the implementation of 3D-MiniNet, a fast and efficient method for semantic segmentation of LIDAR point clouds.

The following figure shows the basic building block of our 3D-MiniNet:

3D-MiniNet overview. It takes P groups of N points each and computes semantic segmentation of the M points of the point cloud where PxN=M.

It consists of two main modules: our proposed learning module (on the left) which learns a 2D tensor which is fed to the second module, an efficient FCNN backbone (on the right) which computes the 2D semantic segmentation. Each 3D point of the point cloud is given a semantic label based on the 2D segmentation.

Code (Pytorch and Tensorflow implementation)

Our PyTorch code is based on Milioto et al. code and the Tensorflow code is based on Biasutti et al. code. For copyright license, please check both code base licenses.

We took their code base and integrate our approach. Therefore, please, consider also citing or checking their work.

Citation

If you find 3D-MiniNet useful, please consider citing:

@article{alonso2020MiniNet3D,
  title={3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation},
  author={Alonso, I{\~n}igo and Riazuelo, Luis and Montesano, Luis and Murillo, Ana C},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020},
  organization={IEEE}
}