681 research outputs found
Network streaming and compression for mixed reality tele-immersion
Bulterman, D.C.A. [Promotor]Cesar, P.S. [Copromotor
A practical comparison between two powerful PCC codec’s
Recent advances in the consumption of 3D content creates the necessity of efficient ways to
visualize and transmit 3D content. As a result, methods to obtain that same content have
been evolving, leading to the development of new methods of representations, namely point
clouds and light fields. A point cloud represents a set of points with associated Cartesian coordinates associated with each point(x, y, z), as well as being able to contain even more information inside that point (color, material, texture, etc). This kind of representation changes
the way on how 3D content in consumed, having a wide range of applications, from videogaming to medical ones. However, since this type of data carries so much information within
itself, they are data-heavy, making the storage and transmission of content a daunting task.
To resolve this issue, MPEG created a point cloud coding normalization project, giving birth
to V-PCC (Video-based Point Cloud Coding) and G-PCC (Geometry-based Point Cloud Coding) for static content. Firstly, a general analysis of point clouds is made, spanning from their
possible solutions, to their acquisition. Secondly, point cloud codecs are studied, namely VPCC and G-PCC from MPEG. Then, a state of art study of quality evaluation is performed,
namely subjective and objective evaluation. Finally, a report on the JPEG Pleno Point Cloud,
in which an active colaboration took place, is made, with the comparative results of the two
codecs and used metrics.Os avanços recentes no consumo de conteúdo 3D vêm criar a necessidade de maneiras eficientes de visualizar e transmitir conteúdo 3D. Consequentemente, os métodos de obtenção
desse mesmo conteúdo têm vindo a evoluir, levando ao desenvolvimento de novas maneiras
de representação, nomeadamente point clouds e lightfields. Um point cloud (núvem de pontos) representa um conjunto de pontos com coordenadas cartesianas associadas a cada ponto
(x, y, z), além de poder conter mais informação dentro do mesmo (cor, material, textura,
etc). Este tipo de representação abre uma nova janela na maneira como se consome conteúdo 3D, tendo um elevado leque de aplicações, desde videojogos e realidade virtual a aplicações médicas. No entanto, este tipo de dados, ao carregarem com eles tanta informação,
tornam-se incrivelmente pesados, tornando o seu armazenamento e transmissão uma tarefa
hercúleana. Tendo isto em mente, a MPEG criou um projecto de normalização de codificação de point clouds, dando origem ao V-PCC (Video-based Point Cloud Coding) e G-PCC
(Geometry-based Point Cloud Coding) para conteúdo estático. Esta dissertação tem como
objectivo uma análise geral sobre os point clouds, indo desde as suas possívei utilizações
à sua aquisição. Seguidamente, é efectuado um estudo dos codificadores de point clouds,
nomeadamente o V-PCC e o G-PCC da MPEG, o estado da arte da avaliação de qualidade, objectiva e subjectiva, e finalmente, são reportadas as actividades da JPEG Pleno Point Cloud,
na qual se teve uma colaboração activa
Geometric Prior Based Deep Human Point Cloud Geometry Compression
The emergence of digital avatars has raised an exponential increase in the
demand for human point clouds with realistic and intricate details. The
compression of such data becomes challenging with overwhelming data amounts
comprising millions of points. Herein, we leverage the human geometric prior in
geometry redundancy removal of point clouds, greatly promoting the compression
performance. More specifically, the prior provides topological constraints as
geometry initialization, allowing adaptive adjustments with a compact parameter
set that could be represented with only a few bits. Therefore, we can envisage
high-resolution human point clouds as a combination of geometric priors and
structural deviations. The priors could first be derived with an aligned point
cloud, and subsequently the difference of features is compressed into a compact
latent code. The proposed framework can operate in a play-and-plug fashion with
existing learning based point cloud compression methods. Extensive experimental
results show that our approach significantly improves the compression
performance without deteriorating the quality, demonstrating its promise in a
variety of applications
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