Efficient processing and feature extraction of largescale 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 cloud points and to extract contour-enhanced features. Specifically, we design hypergraph spectral filters to capture multi-lateral 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.
翻译:这项工作调查基于高光学信号处理(HGSP)的点云再采样,以更好地探索不同云点之间的深层关系,并提取高光增强的特征。具体地说,我们设计高光谱过滤器,以捕捉点云信号节点之间的多边相互作用,并更好地保护其表面轮廓。在没有必要和计算的情况下,首先建立基础高光学,我们低复杂方法通过利用观察到的3D坐标的高光学固定程序直接估计高光谱。用几个尺度评估拟议的重光学采样方法,我们的测试结果验证了点云高光学特征的高度效力,并展示了在噪音观察下基于高光学的重采样的强大性。