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Python bindings for the Point Cloud Library

We are proud to to announce the release of python-pcl Python bindings for PCL.

Now you can use the power and performance of PCL from the comfort of Python. Currently the following features of PCL, using PointXYZ point clouds, are available;

  • I/O and integration; saving and loading PCD files
  • segmentation
  • sample consensus model fittting (RANSAC + others, cylinders, planes, common geometry)
  • smoothing (median least squares)
  • filtering (voxel grid downsampling, passthrough, statistical outlier removal)
  • exporting, importing and analysing pointclouds with numpy

An simple demonstration showing the statistical outlier filter:

import pcl
p = pcl.PointCloud()
p.from_file("table_scene_lms400.pcd")
fil = p.make_statistical_outlier_filter()
fil.set_mean_k (50)
fil.set_std_dev_mul_thresh (1.0)
fil.filter().to_file("inliers.pcd")

For a more complete example showing how to combine filtering, plane and cylinder segmentation (the code used to generate the logo above), see this example.

For more information please see the examples, tests, and API Documentation. This work has been supported by, and is currently in production use at Strawlab.

Article contributed by John Stowers.


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