Hu Wei, Fu Zeqing, Guo Zongming
IEEE Trans Image Process. 2019 Mar 20. doi: 10.1109/TIP.2019.2906554.
As 3D scanning devices and depth sensors mature, point clouds have attracted increasing attention as a format for 3D object representation, with applications in various fields such as tele-presence, navigation and heritage reconstruction. However, point clouds usually exhibit holes of missing data, mainly due to the limitation of acquisition techniques and complicated structure. Further, point clouds are defined on irregular non- Euclidean domains, which is challenging to address especially with conventional signal processing tools. Hence, leveraging on recent advances in graph signal processing, we propose an efficient point cloud inpainting method, exploiting both the local smoothness and the non-local self-similarity in point clouds. Specifically, we first propose a frequency interpretation in graph nodal domain, based on which we derive the smoothing and denoising properties of a graph-signal smoothness prior in order to describe the local smoothness of point clouds. Secondly, we explore the characteristics of non-local self-similarity, by globally searching for the most similar area to the missing region. The similarity metric between two areas is defined based on the direct component and the anisotropic graph total variation of normals in each area. Finally, we formulate the hole-filling step as an optimization problem based on the selected most similar area and regularized by the graph-signal smoothness prior. Besides, we propose voxelization and automatic hole detection methods for the point cloud prior to inpainting. Experimental results show that the proposed approach outperforms four competing methods significantly, both in objective and subjective quality.
随着3D扫描设备和深度传感器的成熟，点云作为一种3D对象表示格式受到了越来越多的关注，其应用于远程呈现、导航和遗产重建等各个领域。然而，点云通常会出现数据缺失的空洞，主要是由于采集技术的限制和结构复杂。此外，点云是在不规则的非欧几里得域上定义的，这对于特别是使用传统信号处理工具来处理具有挑战性。因此，利用图信号处理的最新进展，我们提出了一种有效的点云修复方法，利用点云中的局部平滑性和非局部自相似性。具体来说，我们首先在图节点域中提出一种频率解释，在此基础上，我们推导图信号平滑先验的平滑和去噪特性，以描述点云的局部平滑性。其次，我们通过全局搜索与缺失区域最相似的区域来探索非局部自相似性的特征。两个区域之间的相似性度量是基于每个区域中法线的直接分量和各向异性图总变化来定义的。最后，我们将空洞填充步骤制定为基于所选最相似区域并由图信号平滑先验正则化的优化问题。此外，我们在修复之前为点云提出了体素化和自动空洞检测方法。实验结果表明，所提出的方法在客观和主观质量上均显著优于四种竞争方法。
