Agenda

Or Litany, Tel-Aviv University

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. We propose a paradigm shift and design a structured prediction model in the space of functional maps, linear operators that provide a compact representation of the correspondence. We model the learning process via a deep residual network which takes dense descriptor fields defined on two shapes as input, and outputs a functional map between the two given objects. The resulting correspondence is shown to be accurate on several challenging benchmarks comprising multiple categories, synthetic models, real scans with acquisition artifacts, topological noise, and partiality. 

Bio Sketch

Or Litany is a Ph.D student at the School of Electrical Engineering at Tel-Aviv University, under the supervision of Prof. Alex Bronstein. His research interests lie at the intersection of 3D Shape Analysis, Computer Vision, and Deep Learning.