  Mesh labeling is the key problem of classifying the facets of a 3D mesh with
a label among a set of possible ones. State-of-the-art methods model mesh
labeling as a Markov Random Field over the facets. These algorithms map image
segmentations to the mesh by minimizing an energy function that comprises a
data term, a smoothness terms, and class-specific priors. The latter favor a
labeling with respect to another depending on the orientation of the facet
normals. In this paper we propose a novel energy term that acts as a prior, but
does not require any prior knowledge about the scene nor scene-specific
relationship among classes. It bootstraps from a coarse mapping of the 2D
segmentations on the mesh, and it favors the facets to be labeled according to
the statistics of the mesh normals in their neighborhood. We tested our
approach against five different datasets and, even if we do not inject prior
knowledge, our method adapts to the data and overcomes the state-of-the-art.
