Graph-Based Compression of Dynamic 3D Point Cloud Sequences
TLDR
In this article, a spectral graph wavelet descriptor is used to estimate the motion of 3D point clouds between consecutive frames and a dense motion field is interpolated by solving a graph-based regularization problem.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 share some similarities, 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 point 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 a 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
More filters
Journal ArticleDOI
Graph Signal Processing: Overview, Challenges, and Applications
TL;DR: An overview of core ideas in GSP and their connection to conventional digital signal processing are provided, along with a brief historical perspective to highlight how concepts recently developed build on top of prior research in other areas.
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.
Proceedings ArticleDOI
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
TL;DR: Two new operations to improve PointNet with a more efficient exploitation of local structures are presented, one focuses on local 3D geometric structures and the other exploits local high-dimensional feature structures by recursive feature aggregation on a nearest-neighbor-graph computed from 3D positions.
Journal ArticleDOI
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.
References
More filters
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Proceedings ArticleDOI
Marching cubes: A high resolution 3D surface construction algorithm
TL;DR: In this paper, a divide-and-conquer approach is used to generate inter-slice connectivity, and then a case table is created to define triangle topology using linear interpolation.
Book ChapterDOI
SURF: speeded up robust features
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Book
Spectral Graph Theory
TL;DR: Eigenvalues and the Laplacian of a graph Isoperimetric problems Diameters and eigenvalues Paths, flows, and routing Eigen values and quasi-randomness
Proceedings Article
Learning with Local and Global Consistency
TL;DR: A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points.