2 research outputs found
3D Dynamic Point Cloud Inpainting via Temporal Consistency on Graphs
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.Comment: 7 pages, 5 figures, accepted by IEEE ICME 2020 at 2020.04.03. arXiv
admin note: text overlap with arXiv:1810.0397
Local Frequency Interpretation and Non-Local Self-Similarity on Graph for Point Cloud Inpainting
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 introduce the local 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.Comment: 11 pages, 11 figures, submitted to IEEE Transactions on Image
Processing at 2018.09.0