As a fundamental problem in computer vision, point cloud registration aims to
seek the optimal transformation for aligning a pair of point clouds. In most
existing methods, the information flows are usually forward transferring, thus
lacking the guidance from high-level information to low-level information.
Besides, excessive high-level information may be overly redundant, and directly
using it may conflict with the original low-level information. In this paper,
we propose a novel Iterative Feedback Network (IFNet) for unsupervised point
cloud registration, in which the representation of low-level features is
efficiently enriched by rerouting subsequent high-level features. Specifically,
our IFNet is built upon a series of Feedback Registration Block (FRB) modules,
with each module responsible for generating the feedforward rigid
transformation and feedback high-level features. These FRB modules are cascaded
and recurrently unfolded over time. Further, the Feedback Transformer is
designed to efficiently select relevant information from feedback high-level
features, which is utilized to refine the low-level features. What's more, we
incorporate a geometry-awareness descriptor to empower the network for making
full use of most geometric information, which leads to more precise
registration results. Extensive experiments on various benchmark datasets
demonstrate the superior registration performance of our IFNet.

本文提出了一种新颖的迭代反馈网络（IFNet）用于无监督点云配准，通过重新路由后续的高级特征，高效地丰富了低级特征的表示；进一步设计了反馈变换器以从反馈的高级特征中高效选择相关信息进行细化；最后引入了几何感知描述符来充分利用几何信息，从而获得更精确的配准结果。在各种基准数据集上的广泛实验表明了我们 IFNet 的优异配准性能。