Interactive 3D simulations have enabled break-throughs in robotics and computer vision, but
simulating the broad diversity of environments needed for deep learning requires large …

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

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 …

Recent works in geometric deep learning have introduced neural networks that allow
performing inference tasks on three-dimensional geometric data by defining convolution …

In previous work on point registration, the input point sets are often represented using
Gaussian mixture models and the registration is then addressed through a probabilistic …

Most attempts to represent 3D shapes for deep learning have focused on volumetric grids,
multi-view images and point clouds. In this paper we look at the most popular representation …

This paper presents a comprehensive review and analysis of recent spectral shape
descriptors for nonrigid 3D shape retrieval. More specifically, we compare the latest spectral …

Constrained by the limitations of learning toolkits engineered for other applications, such as
those in image processing, many mesh-based learning algorithms employ data flows that …

Abstract 3D polygon mesh is an important and popular representation of 3D shapes in the
field of computer graphics and computer-aided design. Recent works have introduced deep …

Non-rigid 3D shape retrieval has become an active and important research topic in content-
based 3D object retrieval. The aim of this paper is to measure and compare the performance …