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.

翻译：点云配准在多种计算机视觉任务中起着关键作用，实践中通常需要解决部分重叠配准问题。现有方法大多采用旋转和平移的串行计算方式，同时在配准过程中联合预测重叠部分，这种耦合往往会降低配准性能。本文针对部分到部分配准问题，提出了一种具有双分支解耦的有效配准方法，称为DBDNet。具体地，我们通过分别构建两个独立的对应矩阵，引入双分支结构以消除旋转与平移之间的相互干扰误差。对于部分到部分配准，我们将重叠预测视为配准流程前的预排序任务。据此，我们提出一种重叠预测器，它得益于通过强大的注意力机制实现的显式特征交互，从而能够精确预测逐点掩码。此外，我们设计了一个多分辨率特征提取网络，用于同时捕获局部和全局模式，从而增强重叠预测与配准模块的性能。在合成数据集和真实数据集上的实验结果验证了我们所提出方法的有效性。