IEEE Trans Image Process. 2019 Nov;28(11):5379-5393. doi: 10.1109/TIP.2019.2918735. Epub 2019 May 30.
Inspired by the characteristics of the human visual system, a novel method is proposed for detecting the visually salient regions on 3D point clouds. First, the local distinctness of each point is evaluated based on the difference with its local surroundings. Then, the point cloud is decomposed into small clusters, and the initial global rarity value of each cluster is calculated; a random walk ranking method is then used to introduce cluster-level global rarity refinement to each point in all the clusters. Finally, an optimization framework is proposed to integrate both the local distinctness and the global rarity values to obtain the final saliency detection result of the point cloud. We compare the proposed method with several relevant algorithms and apply it to some computer graphics applications, such as interest point detection, viewpoint selection, and mesh simplification. The experimental results demonstrate the superior performance of the proposed method.
受人类视觉系统特点的启发，提出了一种新的方法来检测 3D 点云上的显著区域。首先，根据每个点与其局部环境的差异来评估其局部显著性。然后，将点云分解为小簇，计算每个簇的初始全局稀有值；然后使用随机游走排名方法对所有簇中的每个点进行簇级全局稀有度细化。最后，提出了一个优化框架，将局部显著度和全局稀有度值集成起来，得到点云的最终显著检测结果。我们将所提出的方法与几种相关算法进行了比较，并将其应用于一些计算机图形学应用，如兴趣点检测、视点选择和网格简化。实验结果表明了所提出的方法的优越性能。
