Rate-Distortion Modeling for Bit Rate Constrained Point Cloud Compression

As being one of the main representation formats of 3D real world and well-suited for virtual reality and augmented reality applications, point clouds have gained a lot of popularity. In order to reduce the huge amount of data, a considerable amount of research on point cloud compression has been done. However, given a target bit rate, how to properly choose the color and geometry quantization parameters for compressing point clouds is still an open issue. In this paper, we propose a rate-distortion model based quantization parameter selection scheme for bit rate constrained point cloud compression. Firstly, to overcome the measurement uncertainty in evaluating the distortion of the point clouds, we propose a unified model to combine the geometry distortion and color distortion. In this model, we take into account the correlation between geometry and color variables of point clouds and derive a dimensionless quantity to represent the overall quality degradation. Then, we derive the relationships of overall distortion and bit rate with the quantization parameters. Finally, we formulate the bit rate constrained point cloud compression as a constrained minimization problem using the derived polynomial models and deduce the solution via an iterative numerical method. Experimental results show that the proposed algorithm can achieve optimal decoded point cloud quality at various target bit rates, and substantially outperform the video-rate-distortion model based point cloud compression scheme.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video Technolog

Lightweight super resolution network for point cloud geometry compression

This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network. The proposed method involves decomposing a point cloud into a base point cloud and the interpolation patterns for reconstructing the original point cloud. While the base point cloud can be efficiently compressed using any lossless codec, such as Geometry-based Point Cloud Compression, a distinct strategy is employed for handling the interpolation patterns. Rather than directly compressing the interpolation patterns, a lightweight super-resolution network is utilized to learn this information through overfitting. Subsequently, the network parameter is transmitted to assist in point cloud reconstruction at the decoder side. Notably, our approach differentiates itself from lookup table-based methods, allowing us to obtain more accurate interpolation patterns by accessing a broader range of neighboring voxels at an acceptable computational cost. Experiments on MPEG Cat1 (Solid) and Cat2 datasets demonstrate the remarkable compression performance achieved by our method.Comment: 10 pages, 3 figures, 2 tables, and 27 reference

Multiscale Latent-Guided Entropy Model for LiDAR Point Cloud Compression

The non-uniform distribution and extremely sparse nature of the LiDAR point cloud (LPC) bring significant challenges to its high-efficient compression. This paper proposes a novel end-to-end, fully-factorized deep framework that encodes the original LPC into an octree structure and hierarchically decomposes the octree entropy model in layers. The proposed framework utilizes a hierarchical latent variable as side information to encapsulate the sibling and ancestor dependence, which provides sufficient context information for the modelling of point cloud distribution while enabling the parallel encoding and decoding of octree nodes in the same layer. Besides, we propose a residual coding framework for the compression of the latent variable, which explores the spatial correlation of each layer by progressive downsampling, and model the corresponding residual with a fully-factorized entropy model. Furthermore, we propose soft addition and subtraction for residual coding to improve network flexibility. The comprehensive experiment results on the LiDAR benchmark SemanticKITTI and MPEG-specified dataset Ford demonstrates that our proposed framework achieves state-of-the-art performance among all the previous LPC frameworks. Besides, our end-to-end, fully-factorized framework is proved by experiment to be high-parallelized and time-efficient and saves more than 99.8% of decoding time compared to previous state-of-the-art methods on LPC compression

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

Network streaming and compression for mixed reality tele-immersion

Bulterman, D.C.A. [Promotor]Cesar, P.S. [Copromotor

Model-based joint bit allocation between geometry and color for video-based 3D point cloud compression

The file attached to this record is the author's final peer reviewed version.In video-based 3D point cloud compression, the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions. Finding an optimal allocation of the total bitrate between the geometry coder and the color coder is a challenging task due to the large number of possible solutions. To solve this bit allocation problem, we first propose analytical distortion and rate models for the geometry and color information. Using these models, we formulate the joint bit allocation problem as a constrained convex optimization problem and solve it with an interior point method. Experimental results show that the rate distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.66% of its time complexity