  In this paper, we study the problem of multi-view sketch correspondence,
where we take as input multiple freehand sketches with different views of the
same object and predict as output the semantic correspondence among the
sketches. This problem is challenging since the visual features of
corresponding points at different views can be very different. To this end, we
take a deep learning approach and learn a novel local sketch descriptor from
data. We contribute a training dataset by generating the pixel-level
correspondence for the multi-view line drawings synthesized from 3D shapes. To
handle the sparsity and ambiguity of sketches, we design a novel multi-branch
neural network that integrates a patch-based representation and a multi-scale
strategy to learn the pixel-level correspondence among multi-view sketches. We
demonstrate the effectiveness of our proposed approach with extensive
experiments on hand-drawn sketches and multi-view line drawings rendered from
multiple 3D shape datasets.
