3 research outputs found
Attribute Compression of 3D Point Clouds Using Laplacian Sparsity Optimized Graph Transform
3D sensing and content capture have made significant progress in recent years
and the MPEG standardization organization is launching a new project on
immersive media with point cloud compression (PCC) as one key corner stone. In
this work, we introduce a new binary tree based point cloud content partition
and explore the graph signal processing tools, especially the graph transform
with optimized Laplacian sparsity, to achieve better energy compaction and
compression efficiency. The resulting rate-distortion operating points are
convex-hull optimized over the existing Lagrangian solutions. Simulation
results with the latest high quality point cloud content captured from the MPEG
PCC demonstrated the transform efficiency and rate-distortion (R-D) optimal
potential of the proposed solutions
A Comprehensive Study and Comparison of Core Technologies for MPEG 3D Point Cloud Compression
Point cloud based 3D visual representation is becoming popular due to its
ability to exhibit the real world in a more comprehensive and immersive way.
However, under a limited network bandwidth, it is very challenging to
communicate this kind of media due to its huge data volume. Therefore, the MPEG
have launched the standardization for point cloud compression (PCC), and
proposed three model categories, i.e., TMC1, TMC2, and TMC3. Because the 3D
geometry compression methods of TMC1 and TMC3 are similar, TMC1 and TMC3 are
further merged into a new platform namely TMC13. In this paper, we first
introduce some basic technologies that are usually used in 3D point cloud
compression, then review the encoder architectures of these test models in
detail, and finally analyze their rate distortion performance as well as
complexity quantitatively for different cases (i.e., lossless geometry and
lossless color, lossless geometry and lossy color, lossy geometry and lossy
color) by using 16 benchmark 3D point clouds that are recommended by MPEG.
Experimental results demonstrate that the coding efficiency of TMC2 is the best
on average (especially for lossy geometry and lossy color compression) for
dense point clouds while TMC13 achieves the optimal coding performance for
sparse and noisy point clouds with lower time complexity.Comment: 17pages, has been accepted by IEEE Transactions on Boradcastin
Graph Signal Processing for Geometric Data and Beyond: Theory and Applications
Geometric data acquired from real-world scenes, e.g, 2D depth images, 3D
point clouds, and 4D dynamic point clouds, have found a wide range of
applications including immersive telepresence, autonomous driving,
surveillance, etc. Due to irregular sampling patterns of most geometric data,
traditional image/video processing methodologies are limited, while Graph
Signal Processing (GSP) -- a fast-developing field in the signal processing
community -- enables processing signals that reside on irregular domains and
plays a critical role in numerous applications of geometric data from low-level
processing to high-level analysis. To further advance the research in this
field, we provide the first timely and comprehensive overview of GSP
methodologies for geometric data in a unified manner by bridging the
connections between geometric data and graphs, among the various geometric data
modalities, and with spectral/nodal graph filtering techniques. We also discuss
the recently developed Graph Neural Networks (GNNs) and interpret the operation
of these networks from the perspective of GSP. We conclude with a brief
discussion of open problems and challenges.Comment: 16 pages, 7 figure