Difference between revisions of "PCL 2016"
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pcl_poisson_reconstruction | pcl_poisson_reconstruction | ||
pcl_xyz2pcd | pcl_xyz2pcd | ||
</pre> | |||
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 (Queueing) System | SLURM job management]] and [[Partitions]] | |||
== Documentation == | == Documentation == | ||
The full documentation can be found at the [https://github.com/PointCloudLibrary/pcl/wiki github wiki]. | The full documentation can be found at the [https://github.com/PointCloudLibrary/pcl/wiki github wiki]. | ||
Latest revision as of 12:25, 15 September 2021
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.
