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

作者： Naik, Deepa Schwarz, Sebastian Vadakita, Vinod Kumar Malamal Roimela, Kimmo Nokia Technol Media Technol Res Tampere Finland Nokia Technol Media Technol Res Munich Germany

ISBN: (纸本)9781728193205

Surface light-field (SLF) is a mapping of a set of color vectors to a set of ray vectors that originate at a point on a surface. It enables rendering photo-realistic view points in extended reality applications. However, the amount of data required to represent SLF is significantly more. Therefore, storing and distributing SLFs requires an efficient compressed representation. The Motion Pictures Experts Group (MPEG) has an on-going standard activity for the compression of point clouds. Until recently, this activity was targeting compression of single texture information, but is now investigating view dependent textures. In this paper, we propose methods to optimize coding of view dependent color without compromising on the visual quality. Our results show the optimizations provided in this paper reduce coded HEVC bit rate by 64% for the all-intra configuration and 52% for the random-access configuration, when compared to coding all texture independently.

关键词： Surface light fields point cloud coding immersive media

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Successive Refinement of Bounding Volumes for point cloud coding 22

Successive Refinement of Bounding Volumes for Point Cloud Co...

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

作者： Tabus, Ioan Kaya, Emre Can Schwarz, Sebastian Tampere Univ Comp Sci Unit Tampere Finland Nokia Technol Media Technol Res Munich Germany

ISBN: (纸本)9781728193205

The paper proposes a new lossy way of encoding the geometry of point clouds. The proposed scheme reconstructs the geometry from only the two depth maps associated to a single projection direction and then proposes a progressive reconstruction process using suitably defined anchor points. The reconstruction from the two depth images follows several primitives for analyzing and encoding, several of which are only optional. The resulting bitstream is embedded and can be truncated at various levels of reconstruction of the bounding volume. The encoding tools for encoding the needed entities are extremely simple and can be combined flexibly. The scheme can also be combined with the G-PCC coding, for reconstructing in a lossless way the sparse point clouds. The experiments show improvement of the rate-distortion performance of the proposed method when combined with the G-PCC codec as compared to G-PCC codec alone.

关键词： point cloud coding lossy geometry compression anchor points

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K-SVD Based point cloud coding for RGB-D Video Compression Using 3D Super-point Clustering 26th

K-SVD Based Point Cloud Coding for RGB-D Video Compression U...

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26th International Conference on MultiMedia Modeling (MMM)

作者： Cheng, Shyi-Chyi Lin, Ting-Lan Tseng, Ping-Yuan Natl Taiwan Ocean Univ Dept Comp Sci & Engn Keelung Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9783030377311;9783030377304

In this paper, we present a novel 3D structure-awareness RGB-D video compression scheme, which applies the proposed 3D super-point clustering to partition the super-points in a colored point cloud, generated from an RGB-D image, into a centroid and a non-centroid super-point datasets. A super-point is a set of 3D points which are characterized with similar feature vectors. Input an RGB-D frame to the proposed scheme, the camera parameters are first used to generate a colored point cloud, which is segmented into multiple super-points using our multiple principal plane analysis (MPPA). These super-points are then grouped into multiple clusters, each of them characterized by a centroid super-point. Next, the median feature vectors of super-points are represented by the K singular value decomposition (K-SVD) based sparse codes. Given a super-point cluster, the sparse codes of the median feature vectors are very similar and thus the redundant information among them are easy to remove by the successive entropy coding. For each super-point, the residual super-point is computed by subtracting the feature vectors inside from the reconstructed median feature vector. These residual feature vectors are also collected and coded using the K-SVD based sparse coding to enhance the quality of the compressed point cloud. This process results in a multiple description coding scheme for 3D point cloud compression. Finally, the compressed point cloud is projected to the 2D image space to obtain the compressed RGB-D image. Experiments demonstrate the effectiveness of our approach which attains better performance than the current state-of-the-art point cloud compression methods.

关键词： point cloud coding RGB-D video compression Sparse representation 3D motion estimation

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Double-Deep Learning-Based point cloud Geometry coding with Adaptive Super-Resolution 10

Double-Deep Learning-Based Point Cloud Geometry Coding with ...

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10th European Workshop on Visual Information Processing (EUVIP)

作者： Ruivo, Manuel Guarda, Andre F. R. Pereira, Fernando Univ Lisbon Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal Inst Telecomunicacoes Lisbon Portugal

ISBN: (数字)9781665466233

ISBN: (纸本)9781665466233

point clouds represent 3D visual data in a very immersive and realistic way, offering to the users a large degree of navigation and interaction. For some key use cases, point clouds are potentially lighter and easier to acquire than other 3D representation models, thus offering an alternative with lower computational cost. To offer visual realistic and immersive experiences, notably the illusion of well-formed surfaces, point clouds typically require a large number of points. To make its storage and transmission feasible, efficient point cloud coding is essential. Recently, deep learning-based point cloud coding solutions have proven to be competitive in compression performance, excelling in distinct scenarios, although struggling to achieve similar results for sparser point clouds and lower coding rates. To tackle these limitations, this paper proposes a double-deep learning-based approach for point cloud coding by integrating a super-resolution tool. The main idea consists on converting sparser point clouds into denser ones via a down-sampling step performed before coding. Since this is a lossy process, a super-resolution step is included after decoding to mitigate the point losses and bringing the point cloud to the initial resolution. Furthermore, the sampling factor can be adaptively selected, thus offering additional flexibility to the point cloud characteristics. The proposed double-deep coding and super-resolution solution outperforms both the G-PCC Octree and V-PCC Intra point cloud coding standards achieving, respectively, 81.9% and 22.3% rate reduction measured as BD-Rate for the PSNR D1 metric.

关键词： Deep learning point cloud coding point cloud super-resolution

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point cloud coding: Adopting a Deep Learningbased Approach

Point Cloud Coding: Adopting a Deep Learningbased Approach

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Picture coding Symposium (PCS)

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)9781728147048

point clouds have recently become an important visual representation format, especially for virtual and augmented reality applications, thus making point cloud coding a very hot research topic. Deep learning-based coding methods have recently emerged in the field of image coding with increasing success. These coding solutions take advantage of the ability of convolutional neural networks to extract adaptive features from the images to create a latent representation that can be efficiently coded. In this context, this paper extends the deep-learning coding approach to point cloud coding using an autoencoder network design. Performance results are very promising, showing improvements over the point cloud Library codec often taken as benchmark, thus suggesting a significant margin of evolution for this new point cloud coding paradigm.

关键词： point cloud coding deep learning convolutional neural network

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Adaptive Deep Learning-Based point cloud Geometry coding

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2021年第2期15卷 415-430页

作者： Guarda, Andre F. R. Rodrigues, Nuno M. M. Pereira, Fernando Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal Inst Telecomunicacoes P-1049001 Lisbon Portugal ESTG Politecn Leiria P-2411901 Leiria Portugal Inst Telecomunicacoes P-2411901 Leiria Portugal

point clouds are a very rich 3D visual representation model, which has become increasingly appealing for multimedia applications with immersion, interaction and realism requirements. Due to different acquisition and creation conditions as well as target applications, point clouds' characteristics may be very diverse, notably on their density. While geographical information systems or autonomous driving applications may use rather sparse point clouds, cultural heritage or virtual reality applications typically use denser point clouds to more accurately represent objects and people. Naturally, to offer immersion and realism, point clouds need a rather large number of points, thus asking for the development of efficient coding solutions. The use of deep learning models for coding purposes has recently gained relevance, with latest developments in image coding achieving state-of-the-art performance, thus making natural the adoption of this technology also for point cloud coding. This paper presents a novel deep learning-based solution for point cloud geometry coding which is able to efficiently adapt to the content's characteristics. The proposed coding solution divides the point cloud into 3D blocks and selects the most suitable available deep learning coding model to code each block, thus maximizing the compression performance. In comparison to the state-of-the-art MPEG G-PCC Trisoup standard, the proposed coding solution offers average quality gains up to 4.9 and 5.7 dB for PSNR D1 and PSNR D2, respectively.

关键词： Three-dimensional displays Encoding Image coding Two dimensional displays Feature extraction Transform coding Geometry point cloud coding deep learning adaptive models

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DEEP LEARNING-BASED point cloud coding: A BEHAVIOR AND PERFORMANCE STUDY 8

DEEP LEARNING-BASED POINT CLOUD CODING: A BEHAVIOR AND PERFO...

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8th European Workshop on Visual Information Processing (EUVIP)

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

ISBN: (纸本)9781728144962

point clouds are an emerging 3D visual representation model for immersive and interactive multimedia applications, in particular for virtual and augmented reality. The huge amount of data associated to point clouds critically asks for efficient point cloud coding technology. While there are already some point cloud coding paradigms in the literature, notably octree, patch and graph-based for geometry data, very recently deep learning emerged in this research domain, offering very promising performances for image coding. While deep learning-based methods often provide interesting results, the understanding of this type of coding solutions is essential to improve their design in order to be used effectively. In this context, this paper presents a study and analysis on the behavior and performance of a deep learning-based point cloud coding solution based on an autoencoder network using only convolutional layers. Beside a promising RD performance, other findings should allow making solid steps in understanding this emerging coding paradigm.

关键词： point cloud coding deep learning convolutional neural network

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Adaptive Multi-level Triangle Soup for Geometry-based point cloud coding 21

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

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IEEE 21st International Workshop on Multimedia Signal Processing (MMSP)

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

ISBN: (纸本)9781728118178

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

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RENDERING-AWARE point cloud coding FOR MIXED REALITY DEVICES 26

RENDERING-AWARE POINT CLOUD CODING FOR MIXED REALITY DEVICES

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26th IEEE International Conference on Image Processing (ICIP)

作者： Capraro, Fabio Milani, Simone Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9781538662496

The recent diffusion of wearable and portable Augmented and Mixed Reality devices have highlighted some open problems in the transmission and visualization of three-dimensional point clouds such as the adaptation of the bit stream to different devices and networks or the optimization of the rendering/coding complexity. The current paper proposes a rendering-aware approach for the compression of static point cloud models that employs a multi-resolution representation of the model in spherical coordinates. This approach proves to be extremely effective in shaping the transmitted bit stream and rendering operations according to the complexity of the 3D model and the available calculation resources. Experimental results show that the proposed solution outperforms one of the most recent state-of-the-art solutions in terms of rate-distortion performance and computional effort.

关键词： point cloud coding spherical coordinates Unity octree coding mixed reality

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DEEP LEARNING-BASED point cloud GEOMETRY coding: RD CONTROL THROUGH IMPLICIT AND EXPLICIT QUANTIZATION

DEEP LEARNING-BASED POINT CLOUD GEOMETRY CODING: RD CONTROL ...

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IEEE International Conference on Multimedia and Expo (ICME)

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

ISBN: (纸本)9781728114859

Deep learning is becoming more and more relevant for multiple multimedia processing tasks, and lately it has raised much interest in the coding arena notably for images and point clouds. While offering near state-of-the-art compression performance, current deep learning-based point cloud coding solutions have a shortcoming since they require training and storing multiple models in order to obtain different rate-distortion trade-offs. This paper proposes a solution that effectively reduces the number of deep learning models that need to be trained and stored by applying explicit quantization to the latent representation, which can be controlled at coding time, to generate varying rate-distortion tradeoffs. The proposed implicit-explicit quantization combination achieves a compression performance that is equivalent or better than the alternative, while significantly reducing the model storage memory requirements.

关键词： point cloud coding deep learning explicit quantization multiple models

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