Modern 3D computer graphics technologies are able to reproduce the dynamics and
appearance of real-world environments and phenomena, building on theoretical models in …

Operator learning refers to the application of ideas from machine learning to approximate
(typically nonlinear) operators mapping between Banach spaces of functions. Such …

We introduce MeshGPT a new approach for generating triangle meshes that reflects the
compactness typical of artist-created meshes in contrast to dense triangle meshes extracted …

Cycle consistency has long been exploited as a powerful prior for jointly optimizing maps
within a collection of shapes. In this paper, we investigate its utility in the approaches of …

We consider the problem of computing dense correspondences between non-rigid shapes
with potentially significant partiality. Existing formulations tackle this problem through heavy …

Motivated by the vast success of deep convolutional networks, there is a great interest in
generalizing convolutions to non-Euclidean manifolds. A major complication in comparison …

In this paper, we propose a transformer-based procedure for the efficient registration of non-
rigid 3D point clouds. The proposed approach is data-driven and adopts for the first time the …

In this work, we present a novel non-rigid shape matching framework based on multi-
resolution functional maps with spectral attention. Existing functional map learning methods …

Abstract 3D shape matching is a long-standing problem in computer vision and computer
graphics. While deep neural networks were shown to lead to state-of-the-art results in shape …

The matching of 3D shapes has been extensively studied for shapes represented as surface
meshes, as well as for shapes represented as point clouds. While point clouds are a …