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 …

Three-dimensional geometric data offer an excellent domain for studying representation
learning and generative modeling. In this paper, we look at geometric data represented as …

Geometric deep learning is an umbrella term for emerging techniques attempting to
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …

We present a simple and efficient method for refining maps or correspondences by iterative
upsampling in the spectral domain that can be implemented in a few lines of code. Our main …

Abstract 3D geometric contents are becoming increasingly popular. In this paper, we study
the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D …

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 present a novel method for computing correspondences across 3D shapes using
unsupervised learning. Our method computes a non-linear transformation of given …

We introduce pointwise map smoothness via the Dirich-let energy into the functional map
pipeline, and propose an algorithm for optimizing it efficiently, which leads to highquality …