Layout Consistent Segmentation of 3-D Meshes via Conditional Random Fields and Spatial Ordering Constraints

Abstract

We address the problem of 3-D Mesh segmentation for categories of objects with known part structure. Part labels are derived from a semantic interpretation of non-overlapping subsurfaces. Our approach models the label distribution using a Conditional Random Field (CRF) that imposes constraints on the relative spatial arrangement of neighboring labels, thereby ensuring semantic consistency. To this end, each label variable is associated with a rich shape descriptor that is intrinsic to the surface. Randomized decision trees and cross validation are employed for learning the model, which is eventually applied using graph cuts. The method is flexible enough for segmenting even geometrically less structured regions and is robust to local and global shape variations.

3D Mesh Segmentation Based on Markov Random Fields and Graph Cuts

“…Lavoué used MRF model to cluster the vertices with the roughness feature first, and then used the region growing method to segment the mesh [9]. Zouhar addressed the problem of 3D mesh segmentation for categories of objects and modeled the label distribution using Conditional Random Field to ensure semantic consistency in segmentation [10].…”