Graph matching, which refers to a class of computational problems of finding an optimal
correspondence between the vertices of graphs to minimize (maximize) their node and edge …

This paper presents a comprehensive literature review on point set registration. The state-of-
the-art modeling methods and algorithms for point set registration are discussed and …

Abstract 3D point cloud registration is a fundamental problem in computer vision and
robotics. Recently, learning-based point cloud registration methods have made great …

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 …

Graph matching refers to finding node correspondence between graphs, such that the
corresponding node and edge's affinity can be maximized. In addition with its NP …

Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix,
which can be generally formulated as Lawler's quadratic assignment problem (QAP). This …

This paper addresses unsupervised discovery and localization of dominant objects from a
noisy image collection with multiple object classes. The setting of this problem is fully …

Graph matching aims to establish node correspondence between two graphs, which has
been a fundamental problem for its NP-hard nature. One practical consideration is the …

Building on recent progress at the intersection of combinatorial optimization and deep
learning, we propose an end-to-end trainable architecture for deep graph matching that …

In this paper, we study a novel and widely existing problem in graph matching (GM), namely,
Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence …