A Data-driven Prior on Facet Orientation for Semantic Mesh Labeling

TL;DR

This paper introduces a data-driven prior for facet orientation in semantic mesh labeling, improving labeling accuracy without scene-specific prior knowledge by leveraging local normal statistics.

Contribution

It proposes a novel energy term that uses local normal statistics as a prior, enhancing mesh labeling without requiring scene-specific information.

Findings

Outperforms state-of-the-art methods on five datasets

Adapts to different datasets without prior scene knowledge

Improves labeling accuracy by leveraging local normal statistics

Abstract

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.