One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point Cloud Registration.

IF 10.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yongzhe Yuan, Yue Wu, Maoguo Gong, Qiguang Miao, A K Qin
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Abstract

The precision of unsupervised point cloud registration methods is typically limited by the lack of reliable inlier estimation and self-supervised signal, especially in partially overlapping scenarios. In this article, we propose an effective inlier estimation method for unsupervised point cloud registration by capturing geometric structure consistency between the source point cloud and its corresponding reference point cloud copy. Specifically, to obtain a high-quality reference point cloud copy, a one-nearest neighborhood (1-NN) point cloud is generated by input point cloud, which facilitates matching map construction and allows for integrating dual neighborhood matching scores of 1-NN point cloud and input point cloud to improve matching confidence. Benefiting from the high-quality reference copy, we argue that the neighborhood graph formed by inlier and its neighborhood should have consistency between source point cloud and its corresponding reference copy. Based on this observation, we construct transformation-invariant geometric structure representations and capture geometric structure consistency to score the inlier confidence for estimated correspondences between source point cloud and its reference copy. This strategy can simultaneously provide the reliable self-supervised signals for model optimization. Finally, we further calculate transformation estimation by the weighted SVD algorithm with the estimated correspondences and the corresponding inlier confidence. We train the proposed model in an unsupervised manner, and extensive experiments on synthetic and real-world datasets illustrate the effectiveness of the proposed method.

用于无监督点云注册的单近邻向导离群值估计。
无监督点云注册方法的精度通常受限于缺乏可靠的离群值估计和自监督信号,特别是在部分重叠的情况下。在本文中,我们通过捕捉源点云与其相应参考点云副本之间的几何结构一致性,为无监督点云注册提出了一种有效的离群值估计方法。具体来说,为了获得高质量的参考点云副本,输入点云会生成一个最近邻域(1-NN)点云,这有助于匹配图的构建,并能整合 1-NN 点云和输入点云的双邻域匹配得分,提高匹配置信度。得益于高质量的参考副本,我们认为由离群点及其邻域形成的邻域图在源点云和其对应的参考副本之间应具有一致性。基于这一观点,我们构建了变换不变的几何结构表示法,并通过捕捉几何结构的一致性来对源点云与其参考副本之间的估计对应关系进行离群可信度评分。这一策略可同时为模型优化提供可靠的自监督信号。最后,我们通过加权 SVD 算法利用估计的对应关系和相应的离群可信度进一步计算变换估计。我们以无监督的方式对所提出的模型进行了训练,并在合成和真实世界数据集上进行了大量实验,证明了所提出方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE transactions on neural networks and learning systems
IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
CiteScore
23.80
自引率
9.60%
发文量
2102
审稿时长
3-8 weeks
期刊介绍: The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.
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