Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

In applications such as social, energy, transportation, sensor, and neuronal networks, high-
dimensional data naturally reside on the vertices of weighted graphs. The emerging field of …

Convolutional Neural Networks are extremely efficient architectures in image and audio
recognition tasks, thanks to their ability to exploit the local translational invariance of signal …

This paper proposes a unified and consistent set of flexible tools to approximate important
geometric attributes, including normal vectors and curvatures on arbitrary triangle meshes …

Modeling tasks, such as surface deformation and editing, can be analyzed by observing the
local behavior of the surface. We argue that defining a modeling operation by asking for …

In this paper, we develop methods to rapidly remove rough features from irregularly
triangulated data intended to portray a smooth surface. The main task is to remove …

To reduce the cost of storing, processing, and visualizing a large-scale point cloud, we
propose a randomized resampling strategy that selects a representative subset of points …

Surface editing operations commonly require geometric details of the surface to be
preserved as much as possible. We argue that geometric detail is an intrinsic property of a …

Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to
undesirable variations in the background scene, MOS becomes very challenging for static …

We present an anisotropic mesh denoising algorithm that is effective, simple and fast. This is
accomplished by filtering vertices of the mesh in the normal direction using local …