Exploring collections of 3D models using fuzzy correspondences

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ShapeNet: An Information-Rich 3D Model Repository (2015, background): “• Rigid Alignments: Establishing a consistent canonical orientation (e.g., upright and front) for every model is important for various tasks such as visualizing shapes [13], shape classiﬁcation [8] and shape recognition [34].”
Learning part-based templates from large collections of 3D shapes (2013, methodology): “…classes and thus should be a valuable resource for data-driven solutions to common geometry processing problems, including surface reconstruction [Kim et al. 2012b], model completion [Shen et al. 2012], model-based object recognition [Shapira et al. 2010; Nan et al. 2012], shape synthesis…”
3D-CODED: 3D Correspondences by Deep Deformation (2018, methodology): “…of this problem have been proposed such as: formulating it as Markov random ﬁeld and using linear programming relaxation [13], optimizing for soft correspondence [40,21,41], re-stricting correspondence space to conformal maps [23,22], heat kernel maps [32], and aligning functional bases [33].”
Image Matching from Handcrafted to Deep Features: A Survey (2020, background): “Another body of work takes the initial (noisy) pairwisematching result as input, and aims to recover a globally consistent pairwise matching set (Kim et al. 2012; Pachauri et al. 2013; Huang and Guibas 2013; Chen et al. 2014; Zhou et al. 2015; Wang et al. 2018; Hu et al. 2018).”