  The existing state-of-the-art point descriptor relies on structure
information only, which omit the texture information. However, texture
information is crucial for our humans to distinguish a scene part. Moreover,
the current learning-based point descriptors are all black boxes which are
unclear how the original points contribute to the final descriptor. In this
paper, we propose a new multimodal fusion method to generate a point cloud
registration descriptor by considering both structure and texture information.
Specifically, a novel attention-fusion module is designed to extract the
weighted texture information for the descriptor extraction. In addition, we
propose an interpretable module to explain the original points in contributing
to the final descriptor. We use the descriptor element as the loss to
backpropagate to the target layer and consider the gradient as the significance
of this point to the final descriptor. This paper moves one step further to
explainable deep learning in the registration task. Comprehensive experiments
on 3DMatch, 3DLoMatch and KITTI demonstrate that the multimodal fusion
descriptor achieves state-of-the-art accuracy and improve the descriptor's
distinctiveness. We also demonstrate that our interpretable module in
explaining the registration descriptor extraction.
