Point cloud registration, which effectively coincides the source and target point clouds, is generally implemented by geometric metrics or feature metrics. In terms of resistance to noise and outliers, feature-metric registration has less error than the traditional point-to-point corresponding geometric metric, and point cloud reconstruction can generate and reveal more potential information during the recovery process, which can further optimize the registration process. In this paper, CFNet, a correspondence-free point cloud registration framework based on feature metrics and reconstruction metrics, is proposed to learn adaptive representations, with an emphasis on optimizing the network. Considering the correlations among the paired point clouds in the registration, a feature interaction module that can perceive and strengthen the information association between point clouds in multiple stages is proposed. To clarify the fact that rotation and translation are essentially uncorrelated, they are considered different solution spaces, and the interactive features are divided into two parts to produce a dual branch regression. In addition, CFNet with its comprehensive objectives estimates the transformation matrix between two input point clouds by minimizing multiple loss metrics. The extensive experiments conducted on both synthetic and real-world datasets show that our method outperforms the existing registration methods.

中文翻译：

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通过特征交互和双分支进行无通信点云注册 [应用说明]


点云配准可以有效地重合源点云和目标点云，通常通过几何度量或特征度量来实现。在抗噪声和异常值方面，特征度量配准比传统的点对点对应几何度量误差更小，并且点云重建可以在恢复过程中生成和揭示更多潜在信息，可以进一步优化配准过程。本文提出了基于特征度量和重建度量的无对应点云配准框架CFNet来学习自适应表示，重点是优化网络。考虑到配准中成对点云之间的相关性，提出了一种能够感知并加强多个阶段点云之间信息关联的特征交互模块。为了澄清旋转和平移本质上不相关的事实，将它们视为不同的解空间，并将交互特征分为两部分以产生双分支回归。此外，CFNet 具有全面的目标，通过最小化多个损失指标来估计两个输入点云之间的变换矩阵。对合成数据集和真实数据集进行的广泛实验表明，我们的方法优于现有的配准方法。