Random walk on point clouds for feature detection

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2025-03-13 DOI:10.1016/j.ins.2025.122082
Yuhe Zhang, Zhikun Tu, Zhi Li, Jian Gao, Bao Guo, Shunli Zhang
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Abstract

The points on the point clouds that can entirely outline the shape of the model are of critical importance, as they serve as the foundation for numerous point cloud processing tasks and are widely utilized in computer graphics and computer-aided design. This study introduces a novel method, RWoDSN, for extracting such feature points, incorporating considerations of sharp-to-smooth transitions, large-to-small scales, and textural-to-detailed features. We approach feature extraction as a two-stage context-dependent analysis problem. In the first stage, we propose a novel neighborhood descriptor, termed the Disk Sampling Neighborhood (DSN), which, unlike traditional spatially and geometrically invariant approaches, preserves a matrix structure while maintaining normal neighborhood relationships. In the second stage, a random walk is performed on the DSN (RWoDSN), yielding a graph-based DSN that simultaneously accounts for the spatial distribution, topological properties, and geometric characteristics of the local surface surrounding each point. This enables the effective extraction of feature points. Experimental results demonstrate that the proposed RWoDSN method achieves a recall of 0.769—22% higher than the current state-of-the-art—alongside a precision of 0.784. Furthermore, it significantly outperforms several traditional and deep-learning techniques across eight evaluation metrics.

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点云上能够完全勾勒出模型形状的点至关重要,因为它们是众多点云处理任务的基础,并广泛应用于计算机制图和计算机辅助设计。本研究介绍了一种提取此类特征点的新方法--RWoDSN,其中考虑了尖锐到平滑的过渡、大尺度到小尺度以及纹理到细节的特征。我们将特征提取作为一个两阶段的上下文相关分析问题来处理。在第一阶段,我们提出了一种名为 "磁盘采样邻域"(Disk Sampling Neighborhood,DSN)的新型邻域描述符,与传统的空间和几何不变方法不同,DSN 在保留矩阵结构的同时,还保持了正常的邻域关系。在第二阶段,对 DSN(RWoDSN)进行随机漫步,生成基于图形的 DSN,同时考虑每个点周围局部表面的空间分布、拓扑特性和几何特征。这样就能有效提取特征点。实验结果表明,所提出的 RWoDSN 方法的召回率达到了 0.769,比目前最先进的方法高出 22%,同时精确度也达到了 0.784。此外,在八个评估指标上,该方法明显优于几种传统技术和深度学习技术。
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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