A Data-driven Prior on Facet Orientation for Semantic Mesh Labeling
This work addresses mesh labeling for 3D scene understanding, but it is incremental as it builds on existing Markov Random Field frameworks.
The paper tackles the problem of semantic mesh labeling by proposing a novel data-driven prior that does not require scene-specific knowledge, and it overcomes state-of-the-art methods when tested on five datasets.
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.