An Efficient Hypergraph Approach to Robust Point Cloud Resampling
An Efficient Hypergraph Approach to Robust Point Cloud Resampling
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DOI：
10.1109/tip.2022.3149225
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发表时间：
2021-03
影响因子：
10.6
通讯作者：
Qinwen Deng;Songyang Zhang;Zhi Ding
中科院分区：
文献类型：
--
作者：
Qinwen Deng;Songyang Zhang;Zhi Ding
大规模点云的高效处理和特征提取在相关计算机视觉和信息物理系统中非常重要。这项工作研究了基于超图信号处理（HGSP）的点云重采样，以更好地探索点云中不同点之间的潜在关系并提取轮廓增强特征。具体而言，我们设计了超图谱滤波器来捕捉点云信号节点之间的多边相互作用，并更好地保留其表面轮廓。我们的方法无需首先构建底层超图及其计算，而是通过利用从观测到的3D坐标得到的超图平稳过程直接估计点云的超图谱。通过多个指标对所提出的重采样方法进行评估，我们的测试结果验证了点云超图表征的高效性，并证明了在有噪声观测情况下基于超图的重采样的稳健性。
Efficient processing and feature extraction of large-scale point clouds are important in related computer vision and cyber-physical systems. This work investigates point cloud resampling based on hypergraph signal processing (HGSP) to better explore the underlying relationship among different points in the point cloud and to extract contour-enhanced features. Specifically, we design hypergraph spectral filters to capture multilateral interactions among the signal nodes of point clouds and to better preserve their surface outlines. Without the need and the computation to first construct the underlying hypergraph, our low complexity approach directly estimates hypergraph spectrum of point clouds by leveraging hypergraph stationary processes from the observed 3D coordinates. Evaluating the proposed resampling methods with several metrics, our test results validate the high efficacy of hypergraph characterization of point clouds and demonstrate the robustness of hypergraph-based resampling under noisy observations.