3D Algorithms and Libraries
3D Libraries
- Curated List of Point Cloud Processing
- PCL - Point Cloud Library is a standalone, large scale, open project for 2D/3D image and point cloud processing.
- 3DTK - The 3D Toolkit provides algorithms and methods to process 3D point clouds.
- pypcd Python module to read and write point clouds stored in the PCD file format, used by the Point Cloud Library.
- Open3D is a Python open-source library that supports rapid development of software that deals with 3D data.
3D Software
- Meshlab - Software to work with PointCloud Data. Can also be used with the Mashlabserver and scripts resp. from the Commandline.
3D Storage
- The PCD (Point Cloud Data) file format
- Octree - hierarchical tree data structure
3D Processing
Object Recognition
Plane Fitting Algorithms
- http://docs.pointclouds.org/trunk/group__surface.html
- https://stackoverflow.com/questions/35726134/3d-plane-fitting-algorithms/41556933#41556933
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RANSAC
- https://stackoverflow.com/questions/28731442/detecting-set-of-planes-from-point-cloud
- https://stackoverflow.com/questions/39159102/fit-a-plane-to-3d-point-cloud-using-ransac
- http://pointclouds.org/documentation/tutorials/random_sample_consensus.php
LOAM Algorithm
LOAM is a method to do LiDAR Odometry mapping. Which means with the help of odometry (positional) data create an consise complete Pointcloud.
SLAM Algorithm
SLAM Stands for Simultaneous localization and mapping. It find feature in the existing data and searches these feature in new data in order to combine them. SLAM is less reliable tham LOAM because it is not relying on odometry data.
- ROS Wiki PCL
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