PCL 2016

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Introduction

The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing. PCL is released under the terms of the BSD license, and thus free for commercial and research use. 1

Installed version(s)

The following versions are installed and currently available...

... on environment hpc-env/8.3:

  • PCL/1.12.0-fosscuda-2019b-Python-3.7.4

Loading / Using PCL

To load the desired version of the module, use the module load command, e.g.

module load hpc-env/8.3
module load PCL

Always remember: this command is case sensitive!

PCL comes with a lot of executables which you can look up after loading PCL as shown above with tht command ls -l $EBROOTPCL/bin
As of version 1.12.0, the following executables are available:

pcl_add_gaussian_noise
pcl_linemod_detection
pcl_openni2_viewer
pcl_progressive_morphological_filter
pcl_boundary_estimation
pcl_local_max
pcl_outlier_removal
pcl_radius_filter
pcl_cluster_extraction
pcl_lum
pcl_outofcore_print
pcl_registration_visualizer
pcl_compute_cloud_error
pcl_marching_cubes_reconstruction
pcl_outofcore_process
pcl_sac_segmentation_plane
pcl_compute_hausdorff
pcl_match_linemod_template
pcl_outofcore_viewer
pcl_spin_estimation
pcl_compute_hull
pcl_mesh2pcd
pcl_passthrough_filter
pcl_tiff2pcd
pcl_concatenate_points_pcd
pcl_mesh_sampling
pcl_pcd2ply
pcl_timed_trigger_test
pcl_converter
pcl_mls_smoothing
pcl_pcd2png
pcl_train_linemod_template
pcl_convert_pcd_ascii_binary
pcl_morph
pcl_pcd2vtk
pcl_train_unary_classifier
pcl_crf_segmentation
pcl_ndt2d
pcl_pcd_change_viewpoint
pcl_transform_from_viewpoint
pcl_crop_to_hull
pcl_ndt3d
pcl_pcd_convert_NaN_nan
pcl_transform_point_cloud
pcl_demean_cloud
pcl_normal_estimation
pcl_pcd_image_viewer
pcl_unary_classifier_segment
pcl_elch
pcl_obj2pcd
pcl_pcd_introduce_nan
pcl_uniform_sampling
pcl_extract_feature
pcl_obj2ply
pcl_pclzf2pcd
pcl_vfh_estimation
pcl_fast_bilateral_filter
pcl_obj2vtk
pcl_plane_projection
pcl_viewer
pcl_fpfh_estimation
pcl_obj_rec_ransac_accepted_hypotheses
pcl_ply2obj
pcl_virtual_scanner
pcl_generate
pcl_obj_rec_ransac_hash_table
pcl_ply2pcd
pcl_vlp_viewer
pcl_gp3_surface
pcl_obj_rec_ransac_model_opps
pcl_ply2ply
pcl_voxel_grid
pcl_grid_min
pcl_obj_rec_ransac_orr_octree
pcl_ply2raw
pcl_voxel_grid_occlusion_estimation
pcl_hdl_grabber
pcl_obj_rec_ransac_orr_octree_zprojection
pcl_ply2vtk
pcl_vtk2obj
pcl_hdl_viewer_simple
pcl_obj_rec_ransac_result
pcl_plyheader
pcl_vtk2pcd
pcl_icp
pcl_obj_rec_ransac_scene_opps
pcl_png2pcd
pcl_vtk2ply
pcl_icp2d
pcl_octree_viewer
pcl_poisson_reconstruction
pcl_xyz2pcd

Since we built PCL with CUDA GPU support, some of the executables need to be executed on a GPU node. Should you encounter the following error message after trying to use one of the executables, you will have to allocate a GPU:

pcl_octree_viewer: error while loading shared libraries: libOpenGL.so.0: cannot open shared object file: No such file or directory

To learn more on how to allocate GPU nodes, you can read the articles about the SLURM job management and Partitions


Documentation

The full documentation can be found at the github wiki.