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 …

We introduce a new framework for learning dense correspondence between deformable 3D
shapes. Existing learning based approaches model shape correspondence as a labelling …

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 …

In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …

In this paper, we propose a generalization of convolutional neural networks (CNN) to non‐
Euclidean domains for the analysis of deformable shapes. Our construction is based on …

We propose a deep learning approach for finding dense correspondences between 3D
scans of people. Our method requires only partial geometric information in the form of two …

In this work, we focus on the task of learning and representing dense correspondences in
deformable object categories. While this problem has been considered before, solutions so …

Techniques from sparse representation have been successfully applied in many areas like
digital image processing, computer vision and pattern recognition in the past ten years …

Point cloud registration is a fundamental task in computer graphics, and more specifically, in
rigid and non-rigid shape matching. The rigid shape matching problem can be formulated as …