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 novel learning-based approach for computing correspondences between non-
rigid 3D shapes. Unlike previous methods that either require extensive training data or …

We consider the problem of computing dense correspondences between non-rigid shapes
with potentially significant partiality. Existing formulations tackle this problem through heavy …

Abstract 3D shape matching is a long-standing problem in computer vision and computer
graphics. While deep neural networks were shown to lead to state-of-the-art results in shape …

The matching of 3D shapes has been extensively studied for shapes represented as surface
meshes, as well as for shapes represented as point clouds. While point clouds are a …

We propose a novel learning-based approach for robust 3D shape matching. Our method
builds upon deep functional maps and can be trained in a fully unsupervised manner …

In this paper, we provide a theoretical foundation for pointwise map recovery from functional
maps and highlight its relation to a range of shape correspondence methods based on …

In the realm of computer vision and graphics accurately establishing correspondences
between geometric 3D shapes is pivotal for applications like object tracking registration …

We propose a novel discrete solver for optimizing functional map‐based energies, including
descriptor preservation and promoting structural properties such as area‐preservation …

In this paper, we explore a new type of extrinsic method to directly align two geometric
shapes with point-to-point correspondences in ambient space by recovering a deformation …