Software Investigate

Learning 3D Mesh Segmentation and Labeling

Download now Free registration required

Executive Summary

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.

  • Format: PDF
  • Size: 3.98 KB