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 …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Three-dimensional surface registration transforms multiple three-dimensional data sets into
the same coordinate system so as to align overlapping components of these sets. Recent …

We present a novel representation of maps between pairs of shapes that allows for efficient
inference and manipulation. Key to our approach is a generalization of the notion of map …

We introduce the first completely unsupervised correspondence learning approach for
deformable 3D shapes. Key to our model is the understanding that natural deformations …

Motivated by the intuition that one can transform two aligned point clouds to each other more
easily and meaningfully than a misaligned pair, we propose CorrNet3D-the first …

Many algorithms for the computation of correspondences between deformable shapes rely
on some variant of nearest neighbor matching in a descriptor space. Such are, for example …

We present a method to match three dimensional shapes under non-isometric deformations,
topology changes and partiality. We formulate the problem as matching between a set of …

Unsupervised point cloud shape correspondence aims to obtain dense point-to-point
correspondences between point clouds without manually annotated pairs. However …