Abstract

head torso upper arm lower arm hand upper leg lower leg foot ear head torso arm leg tail body fin handle cup top base arm lens bridge antenna head thorax leg abdomen cup handle face hair neck fin stabilizer body wing top leg thumb index middle ring pinky palm big roller medium roller axle handle joint jaws head neck torso leg tail ear head torso back upper arm lower arm hand upper leg lower leg foot tail head wing body leg tail big cube small cube back middle seat leg head tentacle Figure 1: Labeling and segmentation results from applying our algorithm to one mesh each from every category in the Princeton Segmentation Benchmark [Chen et al. 2009]. For each result, the algorithm was trained on the other meshes in the same class, e.g., the human was labeled after training on the other meshes in the human class. This paper presents a data-driven approach to simultaneous segmentation and labeling of parts in 3D meshes. An objective function is formulated as a Conditional Random Field model, with terms assessing the consistency of faces with labels, and terms between labels of neighboring faces. The objective function is learned from a collection of labeled training meshes. The algorithm uses hundreds of geometric and contextual label features and learns different types of segmentations for different tasks, without requiring manual parameter tuning. Our algorithm achieves a significant improvement in results over the state-of-the-art when evaluated on the Princeton Segmentation Benchmark, often producing segmentations and labelings comparable to those produced by humans. 1