This repository contains a custom implementation of the Random Sample Consensus (RANSAC) algorithm for fitting a plane on 3D point clouds. The provided code snippet utilizes Open3D to load and visualize a demo point cloud, showcasing the application of the implemented algorithm.
Before running the script, make sure you have the following dependencies installed:
You can install them using:
pip install open3d numpyThe code provides an implementation of the Random Sample Consensus (RANSAC) algorithm for fitting a plane on 3D point clouds. The demo point cloud used in the code is from Open3D, serving as an example that can be easily replaced with your own point cloud data depending on your needs.
The original 3D point cloud data on which a plane is to be fitted. Outliers are present, and the goal is to identify the best-fit plane using RANSAC.
The result after fitting a plane using the RANSAC algorithm. Outliers have been eliminated, and the best-fit plane is highlighted in the point cloud data.
Contributions are welcome! Feel free to open issues or submit pull requests.
This project is licensed under the MIT License - see the LICENSE file for details.