Deep Functional Maps: Structured Prediction for Dense Shape Correspondence

ICCV 2017 Or LitanyTal RemezEmanuele RodolàAlex M. BronsteinMichael M. Bronstein

We introduce a new framework for learning dense correspondence between deformable 3D shapes. Existing learning based approaches model shape correspondence as a labelling problem, where each point of a query shape receives a label identifying a point on some reference domain; the correspondence is then constructed a posteriori by composing the label predictions of two input shapes... (read more)

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