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22nd IEEE International Workshop on Multimedia Signal Processing (MMSP)

作者： Guarda, Andre F. R. Rodrigues, Nuno M. M. Pereira, Fernando Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal Inst Politecn Leiria Inst Telecomunicacoes ESTG Leiria Portugal

ISBN: (纸本)9781728193205

point clouds are a 3D visual representation format that has recently become fundamentally important for immersive and interactive multimedia applications. Considering the high number of points of practically relevant point clouds, and their increasing market demand, efficient point cloud coding has become a vital research topic. In addition, scalability is an important feature for point cloud coding, especially for real-time applications, where the fast and rate efficient access to a decoded point cloud is important;however, this issue is still rather unexplored in the literature. In this context, this paper proposes a novel deep learning-based point cloud geometry coding solution with resolution scalability via interlaced sub-sampling. As additional layers are decoded, the number of points in the reconstructed point cloud increases as well as the overall quality. Experimental results show that the proposed scalable point cloud geometry coding solution outperforms the recent MPEG Geometry-based point cloud Compression standard which is much less scalable.

关键词： point cloud coding deep learning scalable coding resolution scalability interlaced sampling

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Adaptive Multi-level Triangle Soup for Geometry-based point cloud coding

Adaptive Multi-level Triangle Soup for Geometry-based Point ...

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IEEE Workshop on Multimedia Signal Processing

作者： Antoine Dricot João Ascenso Instituto de Telecomunicações Lisboa Portugal Instituto Superior Técnico - Instituto de Telecomunicações Lisboa Portugal

ISBN: (纸本)9781728118185

Nowadays, point clouds are considered as a promising representation for future immersive 3D applications. However, to recreate a 3D object or scene with high fidelity, a large number of points is required, often with high coordinates precision. Thus, efficient compression schemes are much needed for transmission and storage of point cloud data. Several types of coding techniques (e.g. 2D mapping, graph transforms, etc.) have been proposed in the literature to solve this problem. Octree-based solutions provide compression efficiency and level-of-detail scalability, especially for large static point clouds. The upcoming MPEG Geometry based point cloud coding (G-PCC) solution provides high coding performance and relies on a static pruned octree, combined with a trisoup (for triangle soup) surface reconstruction. In this paper, the geometry coding process of G-PCC is enhanced by introducing mode decisions, thus enabling the trisoup in leaf nodes at multiple levels of the octree, i.e. allowing a more adaptive octree partitioning. Experimental results report average BD-rate gains of 5.3% (up to 7%) for the geometry component (point-to-plane error) on dense point clouds, with no significant impact on the color coding performance.

关键词： point cloud coding Adaptive octree G-PCC clouds octrees coding three dimensional shape SOUP Triangle flatness degree error

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Hybrid Octree-Plane point cloud Geometry coding 27

Hybrid Octree-Plane Point Cloud Geometry Coding

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27th European Signal Processing Conference (EUSIPCO)

作者： Dricot, Antoine Ascenso, Joao Inst Telecomunicacoes Lisbon Portugal Inst Telecomunicacoes Inst Super Tecn Lisbon Portugal

ISBN: (纸本)9789082797039

point clouds provide an efficient way of representing 3D data for applications such as virtual or augmented reality, free-viewpoint video, and gaming. However, this rich, realistic and immersive representation demands a very high amount of data and thus efficient point cloud coding solutions are increasingly needed. A promising way to represent point clouds is through octree partitioning, which provides good compression performance with low complexity, as well as level-of-detail scalability. However, other data representations may offer additional benefits to the octree data structure. The goal of this paper is to propose a point cloud geometry coding solution that leverages the adaptive octree partitioning, and enhances it with a novel coding mode. This mode exploits a plane representation for leaf nodes at different layers of the octree. This corresponds to a hybrid solution where two point cloud representation models are combined. The proposed approach can outperform octree based solutions that are currently considered for standardization and it also provides significant compression gains against a static octree solution (average BD-rate gains of 35% are reported).

关键词： point cloud coding Octree Plane

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RATE-DISTORTION DRIVEN ADAPTIVE PARTITIONING FOR OCTREE-BASED point cloud GEOMETRY coding 25

RATE-DISTORTION DRIVEN ADAPTIVE PARTITIONING FOR OCTREE-BASE...

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25th IEEE International Conference on Image Processing (ICIP)

作者： Dricot, Antoine Pereira, Fernando Ascenso, Joao Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9781479970612

point clouds are widely recognized as a promising 3D visual representation model for future 3D applications, e.g. Augmented/Virtual Reality and autonomous mobile navigation. To enable immersive and high quality experiences, a large amount of data needs to be transmitted, and thus, efficient compression techniques are a key factor for its success. Nowadays, many point cloud coding techniques rely on layered octree data structures able to provide multiple benefits, notably level of detail scalability. However, the octree creation process still lacks flexibility compared to the optimal partitioning methods popular in 2D encoders, which are mainly driven by rate-distortion trade-offs. The goal of this paper is to propose a novel octree creation mechanism which is able to exploit the intrinsic point cloud resolution by iteratively partitioning the octree voxels by means of split decisions based on a Rate-Distortion Optimization process. In addition, the arithmetic coding step is adapted to the density at each octree depth. This novel approach allows reaching a significant bitrate reduction, notably 5% in average, and up to 9.8%, over the adopted coding anchor using state-of-the-art octree based coding.

关键词： point cloud coding Octree Rate Distortion Optimization

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point cloud GEOMETRY SCALABLE coding WITH A QUALITY-CONDITIONED LATENTS PROBABILITY ESTIMATOR 31

POINT CLOUD GEOMETRY SCALABLE CODING WITH A QUALITY-CONDITIO...

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2024 International Conference on Image Processing

作者： Mari, Daniele Guarda, Andre F. R. Rodrigues, Nuno M. M. Milani, Simone Pereira, Fernando Univ Padua Padua Italy Univ Lisbon Inst Super Tecn Lisbon Portugal Politecn Leiria ESTG Leiria Portugal Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9798350349405;9798350349399

The widespread usage of point clouds (PC) for immersive visual applications has resulted in the use of very heterogeneous receiving conditions and devices, notably in terms of network, hardware, and display capabilities. In this scenario, quality scalability, i.e., the ability to reconstruct a signal at different qualities by progressively decoding a single bitstream, is a major requirement that has yet to be conveniently addressed, notably in most learning-based PC coding solutions. This paper proposes a quality scalability scheme, named Scalable Quality Hyperprior (SQH), adaptable to learning-based static point cloud geometry codecs, which uses a Quality-conditioned Latents Probability Estimator (QuLPE) to decode a high-quality version of a PC learning-based representation, based on an available lower quality base layer. SQH is integrated in the future JPEG PC coding standard, allowing to create a layered bitstream that can be used to progressively decode the PC geometry with increasing quality and fidelity. Experimental results show that SQH offers the quality scalability feature with very limited or no compression performance penalty at all when compared with the corresponding non-scalable solution, thus preserving the significant compression gains over other state-of-the-art PC codecs.

关键词： point cloud coding scalable coding geometry coding deep learning JPEG PCC standard

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point cloud GEOMETRY AND COLOR coding IN A LEARNING-BASED ECOSYSTEM FOR JPEG coding STANDARDS 30

POINT CLOUD GEOMETRY AND COLOR CODING IN A LEARNING-BASED EC...

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30th IEEE International Conference on Image Processing (ICIP)

作者： Guarda, Andre F. R. Rodrigues, Nuno M. M. Pereira, Fernando Univ Lisbon Inst Super Tecn Lisbon Portugal ESTG Politecn Leiria Leiria Portugal Inst Telecomunicacoes Aveiro Portugal

ISBN: (纸本)9781728198354

Despite its novelty, learning-based coding for images and point clouds is already outperforming some of the best long-standing conventional codecs. In addition to its rising compression performance, learning-based coding has opened new opportunities, notably the use of a single compressed domain representation to provide both high fidelity reconstructions for human visualization as well as effective performance for computer vision tasks, effectively unifying the visual language for man and machine. This paper proposes a new double learning-based static point cloud geometry and color coding solution, which targets point cloud component scalability, rate control flexibility at coding time, and a unified compressed domain representation. The proposed solution exploits the synergies between learning-based coding for images and point clouds, through the current JPEG PCC standard for geometry coding and the JPEG AI standard for image/color coding, establishing a learning-based ecosystem for JPEG coding standards. The proposed solution is able to overcome some design limitations of the current JPEG PCC Verification Model, and significantly improve its RD performance, becoming competitive with MPEG PCC standards.

关键词： point cloud coding geometry and color coding deep learning JPEG standards

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On the stability of point cloud machine learning based coding 10

On the stability of point cloud machine learning based codin...

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10th European Workshop on Visual Information Processing (EUVIP)

作者： Prazeres, Joao Rodrigues, Rafael Pereira, Manuela Pinheiro, Antonio M. G. Univ Beira Interior Covilha Portugal Inst Telecomunicacdes Covilha Portugal

ISBN: (数字)9781665466233

ISBN: (纸本)9781665466233

This paper analyses the performance of two of the most well known deep learning-based point cloud coding solutions, considering the training conditions. Several works have recently been published on point cloud machine learning-based coding, following the recent tendency on image coding. These codecs are typically seen as a set of predefined trained machines. However, the performance of such models is usually very dependent of their training, and little work has been considered on the stability of the codecs' performance, as well as the possible influence of the loss function parameters, and the increasing number of training epochs. The evaluation experiments are supported in a generic test set with point clouds representing objects and also more complex scenes, using the point to point metric (PSNR D1), as several studies revealed the good quality representation of this geometry-only point cloud metric.

关键词： point cloud coding machine learning-based codecs

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point cloud Rendering After coding: Impacts on Subjective and Objective Quality

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IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 4049-4064页

作者： Javaheri, Alireza Brites, Catarina Pereira, Fernando Ascenso, Joao Inst Super Tecn P-1049001 Lisbon Portugal Inst Telecomunicacoes P-1049001 Lisbon Portugal

Recently, point clouds have shown to be a promising way to represent 3D visual data for a wide range of immersive applications, from augmented reality to autonomous cars. Emerging imaging sensors have made easier to perform richer and denser point cloud acquisition, notably with millions of points, thus raising the need for efficient point cloud coding solutions. In such scenario, it is important to evaluate the impact and performance of several processing steps in a point cloud communication system, notably the degradations associated to point cloud coding solutions. Moreover, since point clouds are not directly visualized but rather processed with a rendering algorithm before shown on any display, the perceived quality of point cloud data highly depends on the rendering solution. In this context, the main objective of this paper is to study the impact of several coding and rendering solutions on the perceived user quality and in the performance of available objective assessment metrics. Another contribution regards the assessment of recent MPEG point cloud coding solutions for several popular rendering methods, which was never presented before. The conclusions regard the visibility of three types of coding artifacts for the three considered rendering approaches as well as the strengths and weaknesses of objective metrics when point clouds are rendered after coding.

关键词： Three-dimensional displays Encoding Rendering (computer graphics) Measurement Geometry Quality assessment Codecs point cloud coding quality assessment subjective quality assessment rendering

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Lossless coding of point cloud Geometry Using a Deep Generative Model

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第12期31卷 4617-4629页

作者： Nguyen, Dat Thanh Quach, Maurice Valenzise, Giuseppe Duhamel, Pierre Univ Paris Saclay Lab Signaux & Syst UMR 8506 CNRS Cent Supelec F-91190 Gif Sur Yvette France

This paper proposes a lossless point cloud (PC) geometry compression method that uses neural networks to estimate the probability distribution of voxel occupancy. First, to take into account the PC sparsity, our method adaptively partitions a point cloud into multiple voxel block sizes. This partitioning is signalled via an octree. Second, we employ a deep auto-regressive generative model to estimate the occupancy probability of each voxel given the previously encoded ones. We then employ the estimated probabilities to code efficiently a block using a context-based arithmetic coder. Our context has variable size and can expand beyond the current block to learn more accurate probabilities. We also consider using data augmentation techniques to increase the generalization capability of the learned probability models, in particular in the presence of noise and lower-density point clouds. Experimental evaluation, performed on a variety of point clouds from four different datasets and with diverse characteristics, demonstrates that our method reduces significantly (by up to 37%) the rate for lossless coding compared to the state-of-the-art MPEG codec.

关键词： Three-dimensional displays Octrees Geometry Image coding Encoding Context modeling Convolutional codes point cloud coding deep learning G-PCC context model arithmetic coding

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point cloud GEOMETRY SCALABLE coding WITH A SINGLE END-TO-END DEEP LEARNING MODEL

POINT CLOUD GEOMETRY SCALABLE CODING WITH A SINGLE END-TO-EN...

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IEEE International Conference on Image Processing (ICIP)

作者： Guarda, Andre F. R. Rodrigues, Nuno M. M. Pereira, Fernando Univ Lisbon Inst Super Tecn Lisbon Portugal Inst Telecomunicacoes Aveiro Portugal ESTG Inst Politecn Leiria Leiria Portugal

ISBN: (纸本)9781728163956

point clouds are gaining importance as the format to represent complex 3D objects and scenes, offering high user immersion and interaction, although at the cost of requiring massive data. Scalable coding is an important feature for point cloud coding, especially for real-time applications, where the fast and bitrate efficient access to a decoded point cloud is important;however, this issue is still rather unexplored in the literature. With the rise of deep learning methods as a promising solution for efficient coding, this paper proposes the first deep learning-based point cloud geometry scalable coding solution. Experimental results show that the proposed scalable coding solution consistently outperforms the MPEG standard for static point cloud geometry coding. In this way, a new research path is open for point cloud scalable coding technology.

关键词： point cloud coding deep learning progressive coding quality scalability

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