Since we have relied upon surface meshes as the input representation, it is interesting to ask how recognition performance is affected by changes to the mesh procedure. The most demanding scenario is generalization across mesh procedures, that is, being confronted at recognition time with a mesh of a type essentially different from what training has been based on.
In a final set of experiments, we thus have investigated the effect of
varying the mesh resolution for the test objects. Figure 5(c) shows
plots of correct-classification rates under such conditions, where mesh
resolution is given in percent of the (constant) resolution in the
training phase.
Apparently, recognition performance does not critically depend on test-mesh
resolution. Only below 50% of the training resolution, recognition performance
drops off.
In part, this can be ascribed to the low absolute number of feature samples
drawn.
In particular, the and
criteria exhibit a high
degree of generalization across meshes.