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2024 | Book

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Point Cloud Compression

Technologies and Standardization

Authors: Ge Li, Wei Gao, Wen Gao

Publisher: Springer Nature Singapore

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

3D point clouds have broad applications across various industries and have contributed to advancements in fields such as autonomous driving, immersive media, metaverse, and cultural heritage protection. With the fast growth of 3D point cloud data and its applications, the need for efficient compression technologies has become paramount. This book delves into the forefront of point cloud compression, exploring key technologies, standardization efforts, and future prospects.

This comprehensive book uncovers the foundational concepts, data acquisition methods, and datasets associated with point cloud compression. By examining the fundamental compression technologies, readers can obtain a clear understanding of prediction coding, transform coding, quantization techniques, and entropy coding. Through vivid illustrations and examples, the book elucidates how these techniques have evolved over the years and their potentials for the future. To provide a complete picture, the book presents cutting-edge research methods in point cloud compression and facilitates comparisons among them. Readers can be equipped with an in-depth understanding of the latest advancements, and can gain insights into the various approaches employed in this dynamic field.

Another distinguishing aspect of this book is its exploration of standardization works for point cloud compression. Notable standards, such as MPEG G-PCC, AVS PCC, and MPEG V-PCC, are thoroughly illustrated. By delving into the methods used in geometry-based, video-based, and deep learning-based compression, readers become familiar with the latest breakthroughs in the standard communities.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
As a burgeoning format of spatial data, 3D point cloud has triggered a research boom in the field of vision in recent years. It plays an essential role in many modern industrial applications, such as autonomous driving and virtual/augmented reality. To keep up with the ever-growing application requirements and meet the three progressive goals of ease of storage, transmission, and processing, it is of increasing significance to research and develop efficient compression algorithms to deal with the huge amount of point clouds. Point cloud compression technologies have become research hotspots for the past few years. In this chapter, we first introduce the basic concepts of 3D point clouds and the current mainstream point cloud data acquisition methods. After that, we present several public point cloud benchmark datasets and specific application scenarios of point cloud compression techniques. We further elaborate on the development trends and challenges in point cloud technologies.
Ge Li, Wei Gao, Wen Gao
Chapter 2. Background Knowledge
Abstract
This chapter briefly introduces the fundamentals of information theory, point cloud compression technology, and point cloud quality assessment. The fundamentals of information theory encompass basic concepts related to Shannon’s coding theorems. Point cloud compression technologies include predictive coding, transform coding, quantization, and entropy coding. Point cloud quality assessment covers subjective and objective quality assessment.
Ge Li, Wei Gao, Wen Gao
Chapter 3. Predictive Coding
Abstract
Prediction techniques have been widely used in video coding. Among those techniques, intra prediction is applied to remove spatial redundancy, while inter prediction is adopted to remove temporal redundancy. Point cloud compression based on the hybrid framework follows a similar design philosophy to video compression. As a result, intra and inter prediction are also adopted as essential methods to remove the information redundancy effectively.
Ge Li, Wei Gao, Wen Gao
Chapter 4. Transform Coding
Abstract
The essence of transform coding is to transform signals from one domain (e.g., time domain) to another domain (e.g., frequency domain) with a set of orthogonal basis. The transform is beneficial to eliminate signal correlation and reduce data redundancy. In this chapter, we will introduce some important transforms, including the commonly used discrete cosine transform (DCT), the wavelet transform, and the graph Fourier transform (GFT). We also show several applications of transform-based methods in point cloud attribute compression.
Ge Li, Wei Gao, Wen Gao
Chapter 5. Quantization Techniques
Abstract
Quantization is the process of projecting input values with a large set onto output values with a smaller set, where a typical example is the analog-to-digital conversion. It maps the processed data range from large to small to reduce the data amount. In lossy compression, quantization is the main stage which results in losses of information.
Specific to the field of point cloud compression, quantization includes geometry and attribute quantization. Geometry quantization maps the coordinates of points to a smaller range to facilitate encoding. Attribute quantization mainly maps the prediction residual or the transformed coefficient of the residual to a smaller range. On the decoding side, the data is reconstructed by inverse quantization, which is approximate to the original data.
Ge Li, Wei Gao, Wen Gao
Chapter 6. Entropy Coding
Abstract
As the last stage before the code stream generated by the compression system, entropy coding aims to remove the information redundancy or coding redundancy of the symbols generated in the coding process. Entropy coding is responsible for encoding various transformation coefficients, prediction residuals, motion vectors, and flag information, and it finally generates code streams. This chapter first introduces the basic theory of the main entropy coding technology and then introduces the specific applications of entropy coding in compression systems.
Ge Li, Wei Gao, Wen Gao
Chapter 7. MPEG Geometry-Based Point Cloud Compression (G-PCC) Standard
Abstract
Advances in 3D representation technology have promoted the development of digital museums, automated driving, and other virtual/augmented reality applications. The 3D point cloud is widely used in these emerging applications, thanks to its efficient and concise simulation of 3D objects and scenes, and it provides free views to users via its geometry (the coordinate in 3D space) and attribute (e.g., color, reflectance). Nevertheless, the vast amount of data carried by point clouds limits the deployment of the related applications in terms of efficient communication and storage. To tackle this problem, the Moving Picture Experts Group (MPEG) has launched standardization for directly coding point clouds in 3D space, i.e., geometry-based point cloud compression (G-PCC) codec.
Ge Li, Wei Gao, Wen Gao
Chapter 8. AVS Point Cloud Compression Standard
Abstract
The development of point cloud compression standards has become a necessary method for technological development in emerging fields such as autonomous driving and digital twins. In March 2019, AVS Working Group launched an independent project to develop point cloud compression standard (AVS PCC) to fulfill the requirements of industries in China. AVS PCC standard aims to effectively compress and represent LiDAR point cloud data for both static and dynamic acquisition scenarios in the field of autonomous driving. It also takes into account the compression and representation of point cloud data for digital cultural heritage and dynamic characters. Focusing on the requirements of lossless and limited lossy compression of geometry information, AVS PCC has been developing the first-generation standard, supporting efficient compression of high-precision and high-dimensional point cloud data.
Ge Li, Wei Gao, Wen Gao
Chapter 9. MPEG Video-Based Point Cloud Compression (V-PCC) Standard
Abstract
To achieve efficient compression for 3D dynamic point cloud sequences, MPEG has developed video-based point cloud compression (V-PCC) standard. Specifically, V-PCC projects the 3D point cloud into 2D sequences, i.e., occupancy, geometry, and attribute sequences. The occupancy sequence records the location of projected 2D patches. The geometry sequence reflects the projection depth from the surface point to the projection plane. The attribute sequence stores the attribute information pertaining to the points, which generally is the color value. The projected sequences are compressed by the mature 2D video encoder, which quickly utilizes the existing technologies and completes the application deployment. In this chapter, the coding framework, projection scheme, video generation, and video compression will be introduced in detail.
Ge Li, Wei Gao, Wen Gao
Chapter 10. MPEG AI-Based 3D Graphics Coding Standard
Abstract
Previously, our attention was directed toward techniques related to point cloud compression, encompassing transformation, quantization, entropy coding, and others. Within this section, our emphasis shifts toward methods for point cloud compression rooted in deep learning. Moreover, we delve extensively into the realm of learning-based 3D point cloud compression techniques presented at the MPEG conference. This endeavor aims to foster a more profound comprehension of point cloud compression methodologies.
Ge Li, Wei Gao, Wen Gao
Chapter 11. Future Work
Abstract
Although point cloud compression technologies have achieved excellent performances and attracted much attention, the research is still in its infancy, and there is huge room for improvement. In this chapter, we discuss future works in the relevant research fields, i.e., the main coding tools, Moving Picture Experts Group (MPEG) geometry point cloud compression, MPEG video-based point cloud coding, AVS point cloud compression standard, and deep learning-based point cloud compression.
Ge Li, Wei Gao, Wen Gao
Backmatter
Metadata
Title
Point Cloud Compression
Authors
Ge Li
Wei Gao
Wen Gao
Copyright Year
2024
Publisher
Springer Nature Singapore
Electronic ISBN
978-981-9719-57-0
Print ISBN
978-981-9719-56-3
DOI
https://doi.org/10.1007/978-981-97-1957-0

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