Add multi-mesh functionalities from MeshMath lib and create low-level python modules as part of the SlicerSurfaceToolbox extension

[yehan0223]

We would like to re-implement the functionalities from the original MeshMath lib that are not in the current SurfaceToolbox module. Per our discussion, the functionalities will start to be added by building the lower-level modules, each implementing only a single functionality, which are listed in below.

• Mesh-to-mesh alignment: with/without established correspondence (part of Add filters: avgMesh, alignMesh, GetCurvatures #6)
• Average mesh computation: in Euclidean/Kendall space, non-/normalized (part of Add filters: avgMesh, alignMesh, GetCurvatures #6)
• Mesh-to-mesh surface distance: pointwise or surface without correspondence, relative/absolute, distance statistics
• Mesh data imprinter: to/from mesh, image or file
• Mesh point data operator: support arithmetic point data operations between correspondence established meshes
• Multi-mesh point/face data statistics (variance/mean/attSTD)
• Mesh shape characteristics: kinda like segment statistics but have more shape features, surfaceArea, volumePolyData, mean/Gaussian curvature (see Integrate functionality of SurfaceFeaturesExtractor #7 and part of Add filters: avgMesh, alignMesh, GetCurvatures #6)
• Mesh property statistics (avg/median/min/max/per1/per99OneKWM)

These modules would be very much like the Decimation module, which has its corresponding simpler version in the SurfaceToolBox module. I will start by putting these new modules under SurfaceModels/Advanced just like the Decimation module.

We can revisit the specific usage pipelines to create a high level module later when we finish these low-level yet more in-depth ones. We can easily imagine a typical use pipeline, in which users import a multi-mesh dataset, align them, perform basic statistical calculations on coordinates (distance) or derived properties (curvatures) and imprint/export data to nodes/files. But these can come later when we finish these fundamental ones.

cc: @lassoan @pieper @bpaniagua @jcfr