Point cloud registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose differences, or leverage global shapes, which leads to inconsistency when facing distribution variances such as partial overlapping. Combining the advantages of both types of methods, we adopt a coarse-to-fine pipeline that concurrently handles both issues. We first reduce the pose differences between input point clouds by aligning global features; then we match the local features to further refine the inaccurate alignments resulting from distribution variances. As global feature alignment requires the features to preserve the poses of input point clouds and local feature matching expects the features to be invariant to these poses, we propose an SE(3)-equivariant feature extractor to simultaneously generate two types of features. In this feature extractor, representations that preserve the poses are first encoded by our novel SE(3)-equivariant network and then converted into pose-invariant ones by a pose-detaching module. Experiments demonstrate that our proposed method increases the recall rate by 20% compared to state-of-the-art methods when facing both pose differences and distribution variances.

翻译：点云配准是计算机视觉与机器人领域中的关键问题。现有方法或依赖对姿态差异敏感的局部几何特征匹配，或利用全局形状信息，但在面对部分重叠等分布差异时会导致不一致性。结合两类方法的优势，我们采用一种从粗到细的流水线以同时处理上述问题。首先通过对齐全局特征减小输入点云间的姿态差异，继而匹配局部特征以修正因分布差异导致的错误对齐。由于全局特征对齐要求特征保留输入点云的姿态信息，而局部特征匹配则需要特征对该姿态具有不变性，我们提出了一种SE(3)等变特征提取器以同步生成两类特征。在该特征提取器中，保留姿态的表征首先通过我们提出的新型SE(3)等变网络进行编码，随后经由姿态解耦模块转化为姿态不变表征。实验表明，当同时面临姿态差异与分布差异时，本方法相较现有最优方法将召回率提升了20%。