Learning Hierarchical Shape Segmentation and Labeling from Online Repositories

Understand

We propose a method for converting geometric shapes into hierarchically segmented parts with part labels.

• Our key idea is to train category-specific models from the scene graphs and part names that accompany 3D shapes in public repositories.
• These freely-available annotations represent an enormous, untapped source of information on geometry.
• However, because the models and corresponding scene graphs are created by a wide range of modelers with different levels of expertise, modeling tools, and objectives, these models have very inconsistent segmentations and hierarchies with sparse and noisy textual tags.