  Point cloud registration plays a crucial role in various computer vision
tasks, and usually demands the resolution of partial overlap registration in
practice. Most existing methods perform a serial calculation of rotation and
translation, while jointly predicting overlap during registration, this
coupling tends to degenerate the registration performance. In this paper, we
propose an effective registration method with dual branches decoupling for
partial-to-partial registration, dubbed as DBDNet. Specifically, we introduce a
dual branches structure to eliminate mutual interference error between rotation
and translation by separately creating two individual correspondence matrices.
For partial-to-partial registration, we consider overlap prediction as a
preordering task before the registration procedure. Accordingly, we present an
overlap predictor that benefits from explicit feature interaction, which is
achieved by the powerful attention mechanism to accurately predict pointwise
masks. Furthermore, we design a multi-resolution feature extraction network to
capture both local and global patterns thus enhancing both overlap prediction
and registration module. Experimental results on both synthetic and real
datasets validate the effectiveness of our proposed method.
