Iterative Feedback Network for Unsupervised Point Cloud Registration
Yifan Xie Boyu Wang Shiqi Li Jihua Zhu
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.