LiDAR Exploration with Open3D

Overview

This project focuses on analyzing and visualizing 3D point cloud data from LiDAR datasets utilizing Python and Open3D. It showcases computational techniques for data preparation, voxelization, mesh creation, and transforming local coordinates into geographical coordinates.

Setup

Prerequisites

Ensure Python is installed on your system. This project requires:

• laspy
• Open3D
• numpy
• matplotlib
• folium
• plotly
• pandas
• pyproj
• scikit-learn
• trimesh

Installation

Clone the repository and navigate to the project directory:

git clone https://github.com/WibiSanaSini/LiDAR-Exploration-with-Open3D.git
cd LiDAR-Exploration-with-Open3D

Install required libraries:

pip install laspy open3d numpy matplotlib folium plotly pandas pyproj scikit-learn trimesh

Virtual Environment (optional but recommended):

Create a virtual environment to isolate project dependencies.

1. Use bash python -m venv lidar_env to create a virtual environment named lidar_env.
2. Activate the environment with source lidar_env/bin/activate on Unix/macOS or .\lidar_env\Scripts\activate on Windows.

Running the Project

Data Preparation

1. Place your .las dataset in respective directory.
2. Update the input_file variable in the notebook to the path of your dataset.

Execution

Open the Jupyter Notebook:

jupyter notebook LiDAR_Exploration_Open3D_Notebook.ipynb

Execute the cells sequentially to import libraries, install modules, prepare data, and perform analysis and visualization.

Detailed instruction and reasoning are fully documented inside the notebook.

Features

• Data Preparation: Import and configure LAS dataset.
• Data Analysis: Explore statistical information and visualize point clouds in 3D.
• Voxelization & Mesh Creation: Transform data into voxel grids and meshes.
• Coordinate Transformation: Convert local coordinates into geographical coordinates for accurate mapping.

License

This project is under the MIT License. See the LICENSE file for more details.