Iterative Feedback Network for Unsupervised Point Cloud Registration

• URL: http://arxiv.org/abs/2401.04357v1
• Date: Tue, 9 Jan 2024 04:44:12 GMT
• Title: Iterative Feedback Network for Unsupervised Point Cloud Registration
• Authors: Yifan Xie, Boyu Wang, Shiqi Li and Jihua Zhu
• Abstract summary: We propose a novel Iterative Feedback Network (IFNet) for unsupervised point cloud registration. 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. Our experiments on various benchmark datasets demonstrate the superior registration performance of our IFNet.
• Score: 17.41663459141476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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