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 …

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 …

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 …

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 …

Deep learning has achieved a remarkable performance breakthrough in several fields, most
notably in speech recognition, natural language processing, and computer vision. In …

Feature descriptors play a crucial role in a wide range of geometry analysis and processing
applications, including shape correspondence, retrieval, and segmentation. In this paper, we …

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 …

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

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

The availability of affordable and portable depth sensors has made scanning objects and
people simpler than ever. However, dealing with occlusions and missing parts is still a …