Motion coherence is an important clue for distinguishing true correspondences from false ones. Modeling motion coherence on sparse putative correspondences is challenging due to their sparsity and uneven distributions. Existing works on motion coherence are sensitive to parameter settings and have difficulty in dealing with complex motion patterns. In this paper, we introduce a network called Laplacian Motion Coherence Network (LMCNet) to learn motion coherence property for correspondence pruning. We propose a novel formulation of fitting coherent motions with a smooth function on a graph of correspondences and show that this formulation allows a closed-form solution by graph Laplacian. This closed-form solution enables us to design a differentiable layer in a learning framework to capture global motion coherence from putative correspondences. The global motion coherence is further combined with local coherence extracted by another local layer to robustly detect inlier correspondences. Experiments demonstrate that LMCNet has superior performances to the state of the art in relative camera pose estimation and correspondences pruning of dynamic scenes.

本文提出了一个名为Laplacian Motion Coherence Network（LMCNet）的神经网络，用于学习运动相干性属性，进而对冗余对应进行修剪，该网络使用了图拉普拉斯算子解决了运动相干性模拟中的挑战，通过不同可微层的组合，从候选对应关系中提取局部和全局的运动相干性，从而提高了拍摄动态场景的相机位置估计和冗余对应去除的效果。

可学习的运动相干性用于对应关系剪枝