Learning correspondences aims to find correct correspondences (inliers) from
the initial correspondence set with an uneven correspondence distribution and a
low inlier rate, which can be regarded as graph data. Recent advances usually
use graph neural networks (GNNs) to build a single type of graph or simply
stack local graphs into the global one to complete the task. But they ignore
the complementary relationship between different types of graphs, which can
effectively capture potential relationships among sparse correspondences. To
address this problem, we propose MGNet to effectively combine multiple
complementary graphs. To obtain information integrating implicit and explicit
local graphs, we construct local graphs from implicit and explicit aspects and
combine them effectively, which is used to build a global graph. Moreover, we
propose Graph~Soft~Degree~Attention (GSDA) to make full use of all sparse
correspondence information at once in the global graph, which can capture and
amplify discriminative features. Extensive experiments demonstrate that MGNet
outperforms state-of-the-art methods in different visual tasks. The code is
provided in this https URL

MGNet 通过有效地结合多个补充图表达方法，利用全局图中的所有稀疏通信信息一次性捕捉和放大判别特征，从而在不同的视觉任务中优于现有方法。

MGNet: 通过多个图学习对应关系

MGNet: Learning Correspondences via Multiple Graphs

We propose a novel mixed-integer programming (MIP) formulation for generating
precise sparse correspondences for highly non-rigid shapes. To this end, we
introduce a projected Laplace-Beltrami operator (PLBO) which combines intrinsic
and extrinsic geometric information to measure the deformation quality induced
by predicted correspondences. We integrate the PLBO, together with an
orientation-aware regulariser, into a novel MIP formulation that can be solved
to global optimality for many practical problems. In contrast to previous
methods, our approach is provably invariant to rigid transformations and global
scaling, initialisation-free, has optimality guarantees, and scales to high
resolution meshes with (empirically observed) linear time. We show
state-of-the-art results for sparse non-rigid matching on several challenging
3D datasets, including data with inconsistent meshing, as well as applications
in mesh-to-point-cloud matching.

基于混合整数规划的稀疏对应关系生成方法，结合投影拉普拉斯 - 贝尔特拉米算子、旋转感知正则化器，实现全局最优解，适用于非刚性形状匹配，并具有高效性能。