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As 3D scanning devices and depth sensors mature, point clouds have attractedincreasing attention as a format for 3D object representation, withapplications in various fields such as tele-presence, navigation and heritagereconstruction. However, point clouds usually exhibit holes of missing data,mainly due to the limitation of acquisition techniques and complicatedstructure. Further, point clouds are defined on irregular non-Euclideandomains, which is challenging to address especially with conventional signalprocessing tools. Hence, leveraging on recent advances in graph signalprocessing, we propose an efficient point cloud inpainting method, exploitingboth the local smoothness and the non-local self-similarity in point clouds.Specifically, we first propose a frequency interpretation in graph nodaldomain, based on which we introduce the local graph-signal smoothness prior inorder to describe the local smoothness of point clouds. Secondly, we explorethe characteristics of non-local self-similarity, by globally searching for themost similar area to the missing region. The similarity metric between twoareas is defined based on the direct component and the anisotropic graph totalvariation of normals in each area. Finally, we formulate the hole-filling stepas an optimization problem based on the selected most similar area andregularized by the graph-signal smoothness prior. Besides, we proposevoxelization and automatic hole detection methods for the point cloud prior toinpainting. Experimental results show that the proposed approach outperformsfour competing methods significantly, both in objective and subjective quality.

Local Frequency Interpretation and Non-Local Self-Similarity on Graph for Point Cloud Inpainting