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 …

Before the deep learning revolution, many perception algorithms were based on runtime
optimization in conjunction with a strong prior/regularization penalty. A prime example of this …

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 …

Discrete Laplace operators are ubiquitous in applications spanning geometric modeling to
simulation. For robustness and efficiency, many applications require discrete operators that …

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 …

This paper describes a numerical method for surface parameterization, yielding maps that
are locally injective and discretely conformal in an exact sense. Unlike previous methods for …

A notion of discrete conformality for hyperbolic polyhedral surfaces is introduced in this
paper. This discrete conformality is shown to be computable. It is proved that each …

Scene flow is the three-dimensional (3D) motion field of a scene. It provides information
about the spatial arrangement and rate of change of objects in dynamic environments …

We introduce a novel method for the construction of discrete conformal map**s from
surface meshes of arbitrary topology to the plane. Our approach is based on circle patterns …