Large collections of 3D models are now commonly available via many public repositories, opening new possibilities for data mining, visualization, and synthesis of new models. However, exploring such collections remains challenging because similarity relationships between points on 3D surfaces are often ambiguous and/or difficult to infer automatically. To address this challenge, we introduce an encoding of similarity relationships using fuzzy point correspondences. Based on the observation that correspondence space is low-dimensional, we propose a robust and efficient computational framework to estimate fuzzy correspondences using only a sparse set of pairwise model alignments. We evaluate our algorithm on a range of correspondence benchmarks and report substantial improvements both in terms of accuracy and speed compared to existing alternatives. Further, we use fuzzy correspondences to process large model collections collectively and demonstrate applications towards view alignment, smart exploration, and faceted browsing.