Large collections of 3D models from the same object class (e.g., chairs, cars, animals) are now commonly available via many public repositories, but exploring the range of shape variations across such collections remains a challenging task. In this work, we present a new exploration interface that allows users to browse collections based on similarities and differences between shapes in user-specified regions of interest (ROIs). To support this interactive system, we introduce a novel analysis method for computing similarity relationships between points on 3D shapes across a collection. We encode the inherent ambiguity in these relationships using fuzzy point correspondences and propose a robust and efficient computational framework that estimates fuzzy correspondences using only a sparse set of pairwise model alignments. We evaluate our analysis method on a range of correspondence benchmarks and report substantial improvements in both speed and accuracy over existing alternatives. In addition, we demonstrate how fuzzy correspondences enable key features in our exploration tool, such as automated view alignment, ROI-based similarity search, and faceted browsing.

@article{Kim:2012:ECO,
   author = "Vladimir G. Kim and Wilmot Li and Niloy Mitra and Stephen DiVerdi and
      Thomas Funkhouser",
   title = "Exploring Collections of {3D} Models using Fuzzy Correspondences",
   journal = "Transactions on Graphics (Proc. of SIGGRAPH 2012)",
   year = "2012",
   month = aug,
   volume = "31",
   number = "4"
}