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 …

Researchers have achieved great success in dealing with 2D images using deep learning.
In recent years, 3D computer vision and geometry deep learning have gained ever more …

Representing shapes as level sets of neural networks has been recently proved to be useful
for different shape analysis and reconstruction tasks. So far, such representations were …

Recently, neural networks have been used as implicit representations for surface
reconstruction, modelling, learning, and generation. So far, training neural networks to be …

Point clouds provide a flexible geometric representation suitable for countless applications
in computer graphics; they also comprise the raw output of most 3D data acquisition devices …

We present a simple and general framework for feature learning from point cloud. The key to
the success of CNNs is the convolution operator that is capable of leveraging spatially-local …

We introduce a method for learning to generate the surface of 3D shapes. Our approach
represents a 3D shape as a collection of parametric surface elements and, in contrast to …

Polygonal meshes provide an efficient representation for 3D shapes. They explicitly
captureboth shape surface and topology, and leverage non-uniformity to represent large flat …

Learned 3D representations of human faces are useful for computer vision problems such
as 3D face tracking and reconstruction from images, as well as graphics applications such …

We introduce a new general-purpose approach to deep learning on three-dimensional
surfaces based on the insight that a simple diffusion layer is highly effective for spatial …