  Rejecting correspondence outliers enables to boost the correspondence
quality, which is a critical step in achieving high point cloud registration
accuracy. The current state-of-the-art correspondence outlier rejection methods
only utilize the structure features of the correspondences. However, texture
information is critical to reject the correspondence outliers in our human
vision system. In this paper, we propose General Multimodal Fusion (GMF) to
learn to reject the correspondence outliers by leveraging both the structure
and texture information. Specifically, two cross-attention-based fusion layers
are proposed to fuse the texture information from paired images and structure
information from point correspondences. Moreover, we propose a convolutional
position encoding layer to enhance the difference between Tokens and enable the
encoding feature pay attention to neighbor information. Our position encoding
layer will make the cross-attention operation integrate both local and global
information. Experiments on multiple datasets(3DMatch, 3DLoMatch, KITTI) and
recent state-of-the-art models (3DRegNet, DGR, PointDSC) prove that our GMF
achieves wide generalization ability and consistently improves the point cloud
registration accuracy. Furthermore, several ablation studies demonstrate the
robustness of the proposed GMF on different loss functions, lighting conditions
and noises.The code is available at https://github.com/XiaoshuiHuang/GMF.
