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 …

Abstract We present Lepard, a Learning based approach for partial point cloud matching in
rigid and deformable scenes. The key characteristics are the following techniques that …

This work presents a two-stage neural architecture for learning and refining structural
correspondences between graphs. First, we use localized node embeddings computed by a …

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

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

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 …

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 …