Graph-based compression of dynamic 3D point cloud sequences
TLDR
This is the first paper that exploits both the spatial correlation inside each frame and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way.Abstract:
This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames are similar, motion estimation is key to effective compression of these sequences. It however remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the points clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way.read more
Citations
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Proceedings ArticleDOI
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
TL;DR: FoldingNet as discussed by the authors proposes an end-to-end deep auto-encoder to address unsupervised learning challenges on point clouds, where a folding-based decoder deforms a canonical 2D grid onto the underlying 3D object surface of a point cloud.
Journal ArticleDOI
Emerging MPEG Standards for Point Cloud Compression
Sebastian Schwarz,Marius Preda,Vittorio Baroncini,Madhukar Budagavi,Pablo Cesar,Philip A. Chou,Robert A. Cohen,Maja Krivokuca,Sebastien Lasserre,Zhu Li,Joan Llach,Mammou Khaled,Rufael Mekuria,Ohji Nakagami,Ernestasia Siahaan,Ali Tabatabai,Alexis Michael Tourapis,Vladyslav Zakharchenko +17 more
TL;DR: The main developments and technical aspects of this ongoing standardization effort for compactly representing 3D point clouds, which are the 3D equivalent of the very well-known 2D pixels are introduced.
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Design, Implementation, and Evaluation of a Point Cloud Codec for Tele-Immersive Video
TL;DR: A subjective study in a state-of-the-art mixed reality system shows that introduced prediction distortions are negligible compared with the original reconstructed point clouds and shows the benefit of reconstructed point cloud video as a representation in the 3D virtual world.
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A Graph Signal Processing Perspective on Functional Brain Imaging
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TL;DR: How brain activity can be meaningfully filtered based on concepts of spectral modes derived from brain structure is reviewed and GSP offers a novel framework for the analysis of brain imaging data.
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