A scalable active framework for region annotation in 3D shape collections

Large repositories of 3D shapes provide valuable input for data-driven analysis and modeling tools. They are especially powerful once annotated with semantic information such as salient regions and functional parts. We propose a novel active learning method capable of enriching massive geometric datasets with accurate semantic region annotations. Given a shape collection and a user-specified region label our goal is to correctly demarcate the corresponding regions with minimal manual work. Our active framework achieves this goal by cycling between manually annotating the regions, automatically propagating these annotations across the rest of the shapes, manually verifying both human and automatic annotations,…

• NS
  National Science Foundation
• NS
  Natural Sciences and Engineering Research Council of Canada
• DO
  Division of Mathematical Sciences

  Award: 1521608
• DO
  Division of Information and Intelligent Systems

  Award: 1528025