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Fast Segmentation of 3D Point Clouds A Paradigm on LiDAR Data for Autonomous Vehicle Applications

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Segmentation of 3D Point Cloud

Introduction

Code for implementation of the paper titled : Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications.

Overview of the Repository

In this repo, you'll find :

  • pointclouds: point clouds dataset.
  • paper.pdf: the paper of Fast Segmentation of 3D Point Clouds.
  • gpf.py: ground plane fitting (GPF) algorithm from 3D lidar scan shot in the street.
  • pypcd: folder for mapping between PointField types and numpy types, extracting PointCloud object from a dataframe, etc.

Getting Started

  1. Clone repo: git clone https://github.com/HusseinLezzaik/Segmentation-of-3D-Point-Cloud.git
  2. Install dependencies:
    conda create -n segmentation-point-clouds python=3.7
    conda activate segmentation-point-clouds
    pip install -r requirements.txt
    

Contact

  • Hussein Lezzaik : hussein dot lezzaik at gmail dot com

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Fast Segmentation of 3D Point Clouds A Paradigm on LiDAR Data for Autonomous Vehicle Applications

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