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We proposes a novel and robust 3D object segmentation method, the Gaussian Density Model (GDM) algorithm. The algorithm works with point clouds scanned in the urban environment using the density metrics, based on existing quantity of features in the neighborhood. The LiDAR Velodyne 64E was used to scan urban environment.

Python 100.00%
machine-learning segmentation density-based-clustering urban-environment gdm neighborhood density-metrics python computer-vision

gaussiandensitymodel's Introduction

Gaussian Density Model

Segmentation algorithm of urban environment, we used the LiDAR Velodyne 64E. The algorithm read and write type files csv.

Input: cloud points (x,y,z,d) where d is density and (x,y,z) without density.

Ouput: cloud points (x,y,z,d) objects segmentation.

Required Modules:

  • PIP
  • Numpy
  • Math
  • Pandas
  • CSV
  • Matplotlib
  • Intertools

Installation:

$ sudo apt update

$ sudo apt upgrade

$ pip install numpy

$ pip install math

$ pip install pandas

$ pip install python-csv

$ pip install matplotlib

$ pip install itertools-s

Example:

$ python GDM.py --help

$ python GDM.py -i iValue

Cite article (In review): ========

@article{article, author = {A. R. Pedraza, J. J. G. Barbosa, K. L. F. Rodríguez, A. I. G. Moreno and E. A. G. Barbosa}, year = {2019}, month = {}, pages = {}, title = {Free-form object segmentation in urbanenvironments using Gaussian Density Model}, volume = {}, journal = {Latin America Transactions, IEEE (Revista IEEE America Latina}, doi = {10.1109/TLA.} }

License: AGPL-3

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gaussiandensitymodel's Issues

how can i get the paper

hello,thanks for shareing your code,now , i can not get your paper in IEEE,could you please tell me what should i do?

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