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 …

This paper presents KeypointNet, an end-to-end geometric reasoning framework to learn an
optimal set of category-specific keypoints, along with their detectors to predict 3D keypoints …

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 …

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 …

We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation
and rigid registration framework for multiple input 3D point clouds. The two non-trivial …

This survey gives a selective review of recent development of machine learning (ML) for
combinatorial optimization (CO), especially for graph matching. The synergy of these two …

Graph matching (GM) has been a long-standing combinatorial problem due to its NP-hard
nature. Recently (deep) learning-based approaches have shown their superiority over the …

We present QuantumSync, the first quantum algorithm for solving a synchronization problem
in the context of computer vision. In particular, we focus on permutation synchronization …

This paper considers the setting of jointly matching and clustering multiple graphs belonging
to different groups, which naturally rises in many realistic problems. Both graph matching …

Robust image matching is a fundamental and long-standing open problem in computer
vision. Conventional wisdom has exploited redundancy to improve the robustness of image …