Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …

Important new developments have appeared since the most recent direct survey on shape
correspondence published almost a decade ago. Our survey covers the period from 2011 …

We present a simple, flexible, and general framework titled Partial Registration Network
(PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed learning …

We present a new deep learning approach for matching deformable shapes by introducing
Shape Deformation Networks which jointly encode 3D shapes and correspondences. This is …

This paper introduces a new class of algorithms for optimization problems involving optimal
transportation over geometric domains. Our main contribution is to show that optimal …

Convolutional neural networks have achieved extraordinary results in many computer vision
and pattern recognition applications; however, their adoption in the computer graphics and …

Scaling algorithms for entropic transport-type problems have become a very popular
numerical method, encompassing Wasserstein barycenters, multimarginal problems …

We propose a method for efficiently computing orientation-preserving and approximately
continuous correspondences between non-rigid shapes, using the functional maps …

Many shape and image processing tools rely on computation of correspondences between
geometric domains. Efficient methods that stably extract" soft" matches in the presence of …

We consider the problem of non‐rigid shape matching, and specifically the functional maps
framework that was recently proposed to find correspondences between shapes. A key step …