  Point cloud registration is a fundamental task for estimating rigid
transformations between point clouds. Previous studies have used geometric
information for extracting features, matching and estimating transformation.
Recently, owing to the advancement of RGB-D sensors, researchers have attempted
to utilize visual information to improve registration performance. However,
these studies focused on extracting distinctive features by deep feature
fusion, which cannot effectively solve the negative effects of each feature's
weakness, and cannot sufficiently leverage the valid information. In this
paper, we propose a new feature combination framework, which applies a looser
but more effective fusion and can achieve better performance. An explicit
filter based on transformation consistency is designed for the combination
framework, which can overcome each feature's weakness. And an adaptive
threshold determined by the error distribution is proposed to extract more
valid information from the two types of features. Owing to the distinctive
design, our proposed framework can estimate more accurate correspondences and
is applicable to both hand-crafted and learning-based feature descriptors.
Experiments on ScanNet show that our method achieves a state-of-the-art
performance and the rotation accuracy of 99.1%.
