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This repository contains an implementation of the Iterative Closest Point (ICP) algorithm to align two point clouds, namely the source and target point clouds. The ICP algorithm is widely used for point cloud registration and alignment in various applications.

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Iterative-Closest-Point

License: MIT Version

This repository contains an implementation of the Iterative Closest Point (ICP) algorithm to align two point clouds, namely the source and target point clouds. The ICP algorithm is widely used for point cloud registration and alignment in various applications.

Prerequisites

Before running the script, make sure you have the following dependencies installed:

You can install them using:

pip install open3d numpy

Implementation

The code provides an implementation of the Iterative Closest Point (ICP) algorithm for aligning two point clouds. Customize the script to load your source and target point clouds, then run the ICP algorithm to achieve alignment.

ICP Aligned

The point clouds are successfully aligned. The red surface corresponds to the source point clouds, while the green surface represents the target point clouds.

Contributions

Contributions are welcome! Feel free to open issues or submit pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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This repository contains an implementation of the Iterative Closest Point (ICP) algorithm to align two point clouds, namely the source and target point clouds. The ICP algorithm is widely used for point cloud registration and alignment in various applications.

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