Skeleton-based robust registration framework for corrupted 3D point clouds

IF 6.6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yongqiang Wang , Weigang Li , Wenping Liu , Zhiqiang Tian , Jinling Li
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引用次数: 0

Abstract

Point cloud registration is fundamental in 3D vision applications, including autonomous driving, robotics, and medical imaging, where precise alignment of multiple point clouds is essential for accurate environment reconstruction. However, real-world point clouds are often affected by sensor limitations, environmental noise, and preprocessing errors, making registration challenging due to density distortions, noise contamination, and geometric deformations. Existing registration methods rely on direct point matching or surface feature extraction, which are highly susceptible to these corruptions and lead to reduced alignment accuracy. To address these challenges, a Skeleton-based Robust Registration Framework (SRRF) is presented, which introduces a corruption-resilient skeletal representation to improve registration robustness and accuracy. The framework integrates skeletal structures into the registration process and combines the transformations obtained from both the corrupted point cloud alignment and its skeleton alignment to achieve optimal registration. In addition, a distribution distance loss function is designed to enforce the consistency between the source and target skeletons, which significantly improves the registration performance. This framework ensures that the alignment considers both the original local geometric features and the global stability of the skeleton structure, resulting in robust and accurate registration results. Experimental evaluations on diverse corrupted datasets demonstrate that SRRF consistently outperforms state-of-the-art registration methods across various corruption scenarios, including density distortions, noise contamination, and geometric deformations. The results confirm the robustness of SRRF in handling corrupted point clouds, making it a potential approach for 3D perception tasks in real-world scenarios.
基于骨架的三维点云鲁棒配准框架
点云配准是3D视觉应用的基础,包括自动驾驶、机器人和医学成像,其中多点云的精确对齐对于准确的环境重建至关重要。然而,现实世界的点云经常受到传感器限制、环境噪声和预处理错误的影响,由于密度扭曲、噪声污染和几何变形,使得配准具有挑战性。现有的配准方法依赖于直接点匹配或表面特征提取,这些方法极易受到这些破坏的影响,导致对准精度降低。为了解决这些挑战,提出了一种基于骨架的鲁棒注册框架(SRRF),该框架引入了一种抗腐蚀的骨架表示,以提高注册的鲁棒性和准确性。该框架将骨架结构集成到配准过程中,并结合从损坏点云对齐和骨架对齐中获得的变换来实现最优配准。此外,设计了分布距离损失函数,增强了源骨架和目标骨架的一致性,显著提高了配准性能。该框架既考虑了骨架结构的局部几何特征,又考虑了骨架结构的全局稳定性,保证了配准结果的鲁棒性和准确性。对各种损坏数据集的实验评估表明,SRRF在各种损坏场景(包括密度扭曲、噪声污染和几何变形)中始终优于最先进的配准方法。结果证实了SRRF在处理损坏点云方面的鲁棒性,使其成为现实世界场景中3D感知任务的潜在方法。
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来源期刊
Applied Soft Computing
Applied Soft Computing 工程技术-计算机:跨学科应用
CiteScore
15.80
自引率
6.90%
发文量
874
审稿时长
10.9 months
期刊介绍: Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.
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