  With the development of 3D laser scanning techniques and depth sensors, 3D
dynamic point clouds have attracted increasing attention as a representation of
3D objects in motion, enabling various applications such as 3D immersive
tele-presence, gaming and navigation. However, dynamic point clouds usually
exhibit holes of missing data, mainly due to the fast motion, the limitation of
acquisition and complicated structure. Leveraging on graph signal processing
tools, we represent irregular point clouds on graphs and propose a novel
inpainting method exploiting both intra-frame self-similarity and inter-frame
consistency in 3D dynamic point clouds. Specifically, for each missing region
in every frame of the point cloud sequence, we search for its self-similar
regions in the current frame and corresponding ones in adjacent frames as
references. Then we formulate dynamic point cloud inpainting as an optimization
problem based on the two types of references, which is regularized by a
graph-signal smoothness prior. Experimental results show the proposed approach
outperforms three competing methods significantly, both in objective and
subjective quality.
