This tutorial aims at explaining how to do 3D object recognition in clutter by verifying model hypotheses in cluttered and heavily occluded 3D scenes. After descriptor matching, the tutorial runs one of the Correspondence Grouping algorithms available in PCL in order to cluster the set of point-to-point correspondences, determining instances of object hypotheses in the scene. On these hypotheses, the Global Hypothesis Verification algorithm is applied in order to decrease the amount of false positives.
This tutorial is the follow-up of a previous tutorial on object recognition: :ref:`correspondence_grouping` To understand this tutorial, we suggest first to read and understand that tutorial.
More details on the Global Hypothesis Verification method can be found here: A. Aldoma, F. Tombari, L. Di Stefano, M. Vincze, A global hypothesis verification method for 3D object recognition, ECCV 2012
For more information on 3D Object Recognition in Clutter and on the standard feature-based recognition pipeline, we suggest this tutorial paper: A. Aldoma, Z.C. Marton, F. Tombari, W. Wohlkinger, C. Potthast, B. Zeisl, R.B. Rusu, S. Gedikli, M. Vincze, "Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation", IEEE Robotics and Automation Magazine, 2012
Before starting, you should download from the GitHub folder: Correspondence Grouping the example PCD clouds used in this tutorial (milk.pcd and milk_cartoon_all_small_clorox.pcd), and place the files in the source older.
Then copy and paste the following code into your editor and save it as global_hypothesis_verification.cpp.
.. literalinclude:: sources/global_hypothesis_verification/global_hypothesis_verification.cpp :language: c++ :linenos:
Take a look at the various parts of the code to see how it works.
.. literalinclude:: sources/global_hypothesis_verification/global_hypothesis_verification.cpp :language: c++ :lines: 107-112
.. literalinclude:: sources/global_hypothesis_verification/global_hypothesis_verification.cpp :language: c++ :lines: 142-149
showHelp function prints out the input parameters accepted by the program. parseCommandLine binds the user input with program parameters.
The only two mandatory parameters are model_filename and scene_filename (all other parameters are initialized with a default value).
Other usefuls commands are:
--algorithm (Hough|GC)used to switch clustering algorithm. See :ref:`correspondence_grouping`.-kshows the keypoints used to compute the correspondences
Hypotheses Verification parameters are:
--hv_clutter_reg val: Clutter Regularizer (default 5.0)--hv_inlier_th val: Inlier threshold (default 0.005)--hv_occlusion_th val: Occlusion threshold (default 0.01)--hv_rad_clutter val: Clutter radius (default 0.03)--hv_regularizer val: Regularizer value (default 3.0)--hv_rad_normals val: Normals radius (default 0.05)--hv_detect_clutter val: TRUE if clutter detect enabled (default true)
More details on the Global Hypothesis Verification parameters can be found here: A. Aldoma, F. Tombari, L. Di Stefano, M. Vincze, A global hypothesis verification method for 3D object recognition, ECCV 2012.
.. literalinclude:: sources/global_hypothesis_verification/global_hypothesis_verification.cpp :language: c++ :lines: 60-83
This simple struct is used to create Color presets for the clouds being visualized.
The code below implements a full Clustering Pipeline: the input of the pipeline is a pair of point clouds (the model and the scene), and the output is
std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > rototranslations;
rototraslations represents a list of coarsely transformed models ("object hypotheses") in the scene.
Take a look at the full pipeline:
.. literalinclude:: sources/global_hypothesis_verification/global_hypothesis_verification.cpp :language: c++ :lines: 245-374 :emphasize-lines: 6,9
For a full explanation of the above code see 3D Object Recognition based on Correspondence Grouping.
To improve the coarse transformation associated to each object hypothesis, we apply some ICP iterations.
We create a instances list to store the "coarse" transformations :
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then, we run ICP on the instances wrt. the scene to obtain the registered_instances:
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.. literalinclude:: sources/global_hypothesis_verification/global_hypothesis_verification.cpp :language: c++ :lines: 431-465
GlobalHypothesesVerification takes as input a list of registered_instances and a scene so we can verify() them
to get a hypotheses_mask: this is a bool array where hypotheses_mask[i] is TRUE if registered_instances[i] is a
verified hypothesis, FALSE if it has been classified as a False Positive (hence, must be rejected).
The first part of the Visualization code section is pretty simple, with -k options the program displays goog keypoints in model and in scene
with a styleViolet color.
Later we iterate on instances, and each instances[i] will be displayed in Viewer with a styleRed color.
Each registered_instances[i] will be displayed with two optional colors: styleGreen if the current instance is verified (hypotheses_mask[i] is TRUE), styleCyan otherwise.
.. literalinclude:: sources/global_hypothesis_verification/global_hypothesis_verification.cpp :language: c++ :lines: 467-524
Create a CMakeLists.txt file and add the following lines into it:
.. literalinclude:: sources/global_hypothesis_verification/CMakeLists.txt :language: cmake :linenos:
After you have created the executable, you can then launch it following this example:
>>> ./global_hypothesis_verification milk.pcd milk_cartoon_all_small_clorox.pcd
Original Scene Image
Valid Hypothesis (Green) with simple parameters
You can simulate more false positives by using a larger bin size parameter for the Hough Voting Correspondence Grouping algorithm:
>>> ./global_hypothesis_verification milk.pcd milk_cartoon_all_small_clorox.pcd --cg_size 0.035
Valid Hypothesis (Green) among 9 false positives