Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …

Important new developments have appeared since the most recent direct survey on shape
correspondence published almost a decade ago. Our survey covers the period from 2011 …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Abstract We present Lepard, a Learning based approach for partial point cloud matching in
rigid and deformable scenes. The key characteristics are the following techniques that …

Do visual tasks have a relationship, or are they unrelated? For instance, could having
surface normals simplify estimating the depth of an image? Intuition answers these …

We present a simple, flexible, and general framework titled Partial Registration Network
(PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed learning …

Point clouds provide a flexible geometric representation suitable for countless applications
in computer graphics; they also comprise the raw output of most 3D data acquisition devices …

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 …

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 …

This work presents a two-stage neural architecture for learning and refining structural
correspondences between graphs. First, we use localized node embeddings computed by a …