  Point cloud registration is a fundamental step for many tasks. In this paper,
we propose a neural network named DetarNet to decouple the translation $t$ and
rotation $R$, so as to overcome the performance degradation due to their mutual
interference in point cloud registration. First, a Siamese Network based
Progressive and Coherent Feature Drift (PCFD) module is proposed to align the
source and target points in high-dimensional feature space, and accurately
recover translation from the alignment process. Then we propose a Consensus
Encoding Unit (CEU) to construct more distinguishable features for a set of
putative correspondences. After that, a Spatial and Channel Attention (SCA)
block is adopted to build a classification network for finding good
correspondences. Finally, the rotation is obtained by Singular Value
Decomposition (SVD). In this way, the proposed network decouples the estimation
of translation and rotation, resulting in better performance for both of them.
Experimental results demonstrate that the proposed DetarNet improves
registration performance on both indoor and outdoor scenes. Our code will be
available in \url{https://github.com/ZhiChen902/DetarNet}.
