Cell membranes are vital to shield a cell's interior from the environment. At the same time
they determine to a large extent the cell's mechanical resistance to external forces. In recent …

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 …

Abstract 3D mesh segmentation has become a crucial part of many applications in 3D shape
analysis. In this paper, a comprehensive survey on 3D mesh segmentation methods is …

One of the biggest challenges in non-rigid shape retrieval and comparison is the design of a
shape descriptor that would maintain invariance under a wide class of transformations the …

The computer vision and pattern recognition communities have recently witnessed a surge
of feature-based methods in object recognition and image retrieval applications. These …

We describe a discrete Laplacian suitable for any triangle mesh, including those that are
nonmanifold or nonorientable (with or without boundary). Our Laplacian is a robust drop‐in …

Inspired by recent developments in contraction‐based curve skeleton extraction, we
formulate the skeletonization problem via mean curvature flow (MCF). While the classical …

We present an explicit method to compute a generalization of the Fourier Transform on a
mesh. It is well known that the eigenfunctions of the Laplace Beltrami operator (Manifold …

Shape analysis plays a pivotal role in a large number of applications, ranging from
traditional geometry processing to more recent 3D content management. In this scenario …

In this paper, the problem of non-rigid shape recognition is studied from the perspective of
metric geometry. In particular, we explore the applicability of diffusion distances within the …