Point Cloud library pose estimation given a pre-existing model as truth

Question

PCL's github directs these questions here so I don't really know where else to ask this.

I'm trying to implement pose estimation given a mesh and a generated point cloud. Using PCL, I know you can do pose estimation with two point clouds from the tutorial. In my case I have an accurate faceted model of my target object. Does there exist a PCL pose estimator that can consume faceted truth models? I would like to avoid using mesh_sampling or mesh2pcd as a work around.

Googling does not bring any results relevant to my search with the following 54 terms

• point cloud library pose estimation with

  • mesh
  • triangles
  • facets
  • truth data
  • model truth data
  • model
  • mesh truth data
  • vertexes
  • vertices

• point cloud library point set registration with

  • (above)

• point cloud library registration with

  • (above)

• point cloud library 6DOF with

  • (above)

• point cloud library pose with

  • (above)

• point cloud library orientation with

  • (above)

Maybe I don't know the right words to search?

but it appears like it might be possible, because functors like this

 pcl::SampleConsensusPrerejective<PointNT,PointNT,FeatureT>

and this

 pcl::Registration< PointSource, PointTarget, Scalar >

take what seem to be pretty generic template arguments, only requiring PCL base functionality. But placing pcl::mesh did not compile (though it doesn't appear to be the only "mesh" type in PCL), since mesh doesn't seem to inherit off of base. The documentation does not talk about what is or is not possible with template types. Additionally I have found zero documentation that states this is impossible or indicates that only point clouds are allowed.

Can I use the model directly with out point cloud conversion, and if not why?

Answer 1

PCL is a library for point cloud processing. While some mesh support is available (pcl::PolygonMesh), practically all the implemented algorithms are based on point cloud data.

However keep in mind that a mesh is just a point cloud + additional triangulation information (faces) - so this means that any point cloud algorithm can be applied on a mesh. You just need to generate a point cloud from your mesh's vertices, and ignore the faces - no need for mesh sampling.