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Scalable Point Cloud attribute compression

IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 889-899页

作者： Zhang, Junteng Wang, Jianqiang Ding, Dandan Ma, Zhan Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Peoples R China

This article develops a Scalable Point Cloud attribute compression solution, termed ScalablePCAC. In a two-layer example, ScalablePCAC uses the standard G-PCC at the base layer to directly encode the thumbnail point cloud that is downscaled from the original input, and a learning-based model at the enhancement layer to compress and restore the full-resolution input point cloud conditioned on the base layer reconstruction. As such, the base layer provides a coarse reconstruction of the input point cloud and the enhancement layer further improves the quality. We then adopt a cross-layer rate allocation strategy that flexibly determines the resolution downscaling factor, the quantization parameter of the base layer, and the quality controlling factor of the enhancement layer to adapt the bitrate of the two layers for approximately optimal Rate-Distortion (R-D) performance. We conduct extensive experiments on popular point clouds following the MPEG common test conditions. Results demonstrate that the proposed ScalablePCAC achieves >10% BD-BR reduction against the latest G-PCC version 22 (TMC13v22) on the Y component;it also significantly outperforms existing learning-based solutions for point cloud attribute compression, e.g., compared with a recent work showing state-of-the-art performance, it achieves >20% BD- BR reduction.

关键词： Point cloud compression Image coding Transform coding Three-dimensional displays Image reconstruction Scalability Rate-distortion 3D Point cloud attribute compression scalability MPEG G-PCC

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SPAC: Sampling-based Progressive attribute compression for Dense Point Clouds

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IEEE Transactions on Image Processing 2025年 34卷 2939-2953页

作者： Mao, Xiaolong Yuan, Hui Guo, Tian Jiang, Shiqi Hamzaoui, Raouf Kwong, Sam Shandong University School of Software Jinan 250100 China Shandong University School of Control Science and Engineering 250061 China Shandong University School of Control Science and Engineering Jinan 250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan 250061 China De Montfort University School of Engineering and Sustainable Development Leicester LE1 9BH United Kingdom Lingnan University School of Data Science Hong Kong

We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with geometry assistance, and a global hyperprior entropy model. The frequency sampling module uses a Hamming window and the Fast Fourier Transform to extract high-frequency components of the point cloud. The difference between the original point cloud and the sampled point cloud is divided into multiple sub-point clouds. These sub-point clouds are then partitioned using an octree, providing a structured input for feature extraction. The feature extraction module integrates adaptive convolutional layers and uses offset-attention to capture both local and global features. Then, a geometry-assisted attribute feature refinement module is used to refine the extracted attribute features. Finally, a global hyperprior model is introduced for entropy encoding. This model propagates hyperprior parameters from the deepest (base) layer to the other layers, further enhancing the encoding efficiency. At the decoder, a mirrored network is used to progressively restore features and reconstruct the color attribute through transposed convolutional layers. The proposed method encodes base layer information at a low bitrate and progressively adds enhancement layer information to improve reconstruction accuracy. Compared to the best anchor of the latest geometry-based point cloud compression (G-PCC) standard that was proposed by the Moving Picture Experts Group (MPEG), the proposed method can achieve an average Bjøntegaard delta bitrate of -24.58% for the Y component (resp. -21.23% for YUV components) on the MPEG Category Solid dataset and -22.48% for the Y component (resp. -17.19% for YUV components) on the MPEG Category Dense dataset. This is the first instance that a learning-based attribute codec outperforms the G-PCC standard on these datasets by following the common test conditions specified by MPEG. ©

关键词： attribute compression augmented reality immersive communication metaverse point cloud compression point cloud sampling scalable coding

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Fine-Grained Patch Segmentation and Rasterization for 3-D Point Cloud attribute compression

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第12期31卷 4590-4602页

作者： Zhao, Baoquan Lin, Weisi Lv, Chenlei Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

Due to the high dimensionality of point cloud data and the irregularity and complexity of its geometric structure, effective attribute compression remains a very challenging task. Many recent efforts have focused on transforming point clouds into images and leveraging existing sophisticated image/video codecs to improve attribute coding efficiency. However, how to synthesize coherent and correlation-preserving attribute images is still inadequately addressed by existing studies, which are hindering the exertion of the merits of well-developed compression infrastructure. In this paper, we present a novel image synthesis method for effective point cloud attribute compression. Firstly, the proposed scheme segments a given point cloud into a collection of fine-grained patches by performing geometric structure analysis using heat kernel signature feature descriptor and complex points;Secondly, we transform the obtained patches from 3-D to 2-D using a low-dimensional embedding algorithm and then convert them into patch attribute images with the proposed patch rasterization and rectification method;And finally, we compactly assemble all the attribute images of patches together by formulating it as a bin nesting problem and harvest an attribute image of the whole point cloud for image/video-based compression. Experimental results demonstrate the effectiveness of the proposed method in point cloud attribute compression and its superiority over state-of-the-art codecs. The source code of this work is publicly available at https://***/pccompession/UPCAC.

关键词： Three-dimensional displays Image coding Image segmentation Image synthesis Encoding Transform coding Solid modeling 3-D point cloud attribute compression patch segmentation attribute image synthesis

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Hilbert Space Filling Curve Based Scan-Order for Point Cloud attribute compression

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 4609-4621页

作者： Chen, Jiafeng Yu, Lu Wang, Wenyi Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China

Point cloud is a set of three-dimensional points in arbitrary order, which is a popular representation of 3D scene in autonomous navigation and immersive applications in recent years. compression becomes an inevitable issue due to the huge data volume of point cloud. In order to effectively compress attributes of those points, proper reordering is important. The existing voxel-based point cloud attributes compression scheme uses a naive scan for points reordering. In this paper, we theoretically analyzed 3C properties of point cloud, i.e., Compactness, Clustering and Correlation, of different scan-orders defined by different space filling curves and disclosed that the Hilbert curve can provide the best spatial correlation preservation compared with Z-order and Gray-coded curves. It is also statistically verified that the Hilbert curve always has the best ability of attributes correlation preservation for point clouds with different sparsity. We also proposed a fast and iterative Hilbert address code generation method to implement points reordering. The Hilbert scan-order could be combined with various point cloud attribute coding methods. Experiments show that the correlation preservation feature of the proposed scan-order can bring us 6.1% and 6.5% coding gain for prediction and transform coding, respectively.

关键词： Point cloud compression Image coding Three-dimensional displays Codes Correlation Filling Transform coding Point cloud attribute compression Hilbert curve GPCC correlation preservation

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Cluster-based two-branch framework for point cloud attribute compression

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VISUAL COMPUTER 2024年第9期40卷 5947-5960页

作者： Sun, Longhua Wang, Jin Zhu, Qing Liu, Jiaying Yu, Jiawen Beijing Univ Technol Fac Informat Technol 100 Pingleyuan Beijing 100124 Peoples R China

Owing to the irregular distribution of point clouds in 3D space, effectively compressing the point cloud is still challenging. Recently, numerous compression methods have been developed with outstanding performance for the compression of geometry information of point clouds. On the contrary, limited explorations have been devoted to point cloud attribute compression (PCAC). Thus, this paper focuses on the study of point cloud attribute compression by applying geometric information as decoded prior information. In this paper, a novel cluster-based two-branch framework for PCAC is proposed. Specifically, the point cloud is first divided into adaptive un-overlapped blocks via K-means according to geometric information, which enables an efficient local representation of the point cloud. Then, we split the point cloud attributes into two components(Block-Mean component and Block-Residual component) and compressed them separately through the designed block-based dual branches. For the Block-Mean component, we design a prediction scheme to remove the inter-block attribute redundancy. The Block-Residual component is further compacted by applying a designed graph Fourier transform generated by geometric prior information. Then, the transform coefficients are encoded using an arithmetic coder. Extensive experimental results demonstrate that the proposed point cloud attribute compression scheme outperforms existing attribute compression schemes in terms of both objective quality and subjective quality.

关键词： Point cloud compression attribute compression Graph Laplacian transform Intra-prediction

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Early Termination of Dyadic Region-Adaptive Hierarchical Transform for Efficient attribute compression of 3D Point Clouds

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IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 214-218页

作者： Hooda, Reetu Pan, W. David Univ Alabama Huntsville Dept Elect & Comp Engn Huntsville AL 35816 USA

Thelow-complexity RAHT (Region-Adaptive Hierarchical Transform) and its corresponding Dyadic decomposition have been incorporated in the MPEG (Moving Picture Expert Group) standardization for encoding the attribute values in 3D point clouds. While significant gains were observed with the replacement of RAHT by Dyadic RAHT, the standard scheme uses only one type of decomposition for the entire point cloud. In this letter, we propose a novel adaptive scheme of switching between RAHT and Dyadic RAHT to achieve early termination of Dyadic RAHT by using 3D gradient filters. Improvements were made on the state-of-the-art scheme that uses only Dyadic RAHT transformation. The proposed method was tested on the publicly available 3D point cloud datasets. The results shows we can achieve cumulative compression gain of up to 11% over all-dyadic approach, by avoiding further decomposition in some cases.

关键词： Point cloud compression Three-dimensional displays Transforms Transform coding Image edge detection Switches Standards attribute compression 3D Sobel filter point clouds RAHT Dyadic RAHT

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DISTRIBUTION-DRIVEN PREDICTOR SCREENING FOR POINT CLOUD attribute compression 29

DISTRIBUTION-DRIVEN PREDICTOR SCREENING FOR POINT CLOUD ATTR...

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IEEE International Conference on Image Processing (ICIP)

作者： Lu, Jingyun Zhang, Wei Yang, Lihui Yang, Fuzheng Xidian Univ Telecommun Engn Xian Peoples R China Peng Cheng Lab Artificial Intelligence Res Ctr Shenzhen Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

With the rapid development of 3D sensing and reconstruction technologies, point cloud compression has become an active research area. When designing compression methods for point cloud attributes, the reconstructed geometry information is commonly used to guide the attribute compression. Specifically, the geometric distance between points is widely applied in different forms to assist attribute prediction. However, due to the complicated spatial distributions of 3D point clouds, using distance as the sole feature may not be optimal. In this paper, the spatial distribution of point clouds is further considered to derive a distribution-driven attribute prediction. Experimental results show that the proposed method can significantly improve the coding efficiency of G-PCC. The proposed method has been adopted in the technologies under consideration (TuC) for MPEG G-PCC and will be included in the second version of the G-PCC specification.

关键词： Point cloud compression attribute compression distribution-driven geometry-guide

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Point Cloud attribute compression via Clustering and Intra Prediction 13

Point Cloud Attribute Compression via Clustering and Intra P...

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13th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Zhang, Ke Zhu, Wenjie Xu, Yiling Liu, Ning Shanghai Jiao Tong Univ Cooperat MediaNet Innovat Ctr Shanghai Peoples R China

ISBN: (纸本)9781538647295

With the rapid development of 3D capture technologies, point cloud has been widely used in many emerging applications such as augmented reality, autonomous driving, and 3D printing. However, point cloud, used to represent real world objects in these applications, may contain millions of points, which results in huge data volume. Therefore, efficient compression algorithms are essential for point cloud when it comes to storage and real-time transmission issues. Specially, the attribute compression of point cloud is still challenging owing to the sparsity and irregular distribution of corresponding points in 3D space. In this paper, we present a novel point cloud attribute compression scheme based on hierarchical clustering and 3D intra prediction. Unlike the commonly used octree decomposition based compression approaches, we divide point cloud into distinguishing clusters by a hierarchical clustering algorithm. Accordingly, a genetic algorithm based intra prediction is introduced to organize the irregularly distributed points in each cluster, and the color attributes of these points are mapped to 2D uniform grids. Finally, a well-developed image coding method is leveraged to achieve adorable compression performance. Experimental results demonstrate that the proposed scheme is much more efficient than traditional attribute compression schemes.

关键词： point cloud attribute compression clustering intra prediction

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PDE-based Progressive Prediction Framework for attribute compression of 3D Point Clouds 23

PDE-based Progressive Prediction Framework for Attribute Com...

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31st ACM International Conference on Multimedia (MM)

作者： Yang, Xiaodong Shao, Yiting Liu, Shan Li, Thomas H. Li, Ge Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Artificial Intelligence Res Ctr Shenzhen Peoples R China Tencent Media Lab Palo Alto CA USA

ISBN: (纸本)9798400701085

In recent years, the diffusion-based image compression scheme has achieved significant success, which inspires us to use diffusion theory to employ the diffusion model for point cloud attribute compression. However, the relevant existing methods cannot be used to deal with our task due to the irregular structure of point clouds. To handle this, we propose the partial differential equation (PDE) based progressive prediction framework for attribute compression of 3D point clouds. Firstly, we propose a PDE-based prediction module, which performs prediction by optimizing attribute gradients, allowing the geometric distribution of adjacent areas to be fully utilized and explaining the weighting method for prediction. Besides, we propose a low-complexity method for calculating partial derivative operations on point clouds to address the uncertainty of neighbor occupancy in three-dimensional space. In the proposed prediction framework, we design a two-layer level of detail (LOD) structure, where the attribute information in the high level is used for interpolating the low level by edge-enhancing anisotropic diffusion (EED) to infer local features from the high-level information. After the diffusion-based interpolation, we design a texture-wise prediction method making use of interpolated values and texture information. Experiment results show that our proposed framework achieves an average of 12.00% BD-rate reduction and 1.59% bitrate saving compared with Predlift (PLT) under attribute near-lossless and attribute lossless conditions, respectively. Furthermore, additional experiments demonstrate our proposed scheme has better texture preservation and subjective quality.

关键词： Point cloud attribute compression Intra prediction Partial differential equations Diffusion

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Lossless Point Cloud attribute compression with Normal-based Intra Prediction 16

Lossless Point Cloud Attribute Compression with Normal-based...

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16th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (IEEE BMSB)

作者： Yin, Qian Ren, Qingshan Zhao, Lili Wang, Wenyi Chen, Jianwen Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781665449083

The sparse LiDAR point clouds become more and more popular in various applications, e.g., the autonomous driving. However, for this type of data, there exists much under-explored space in the corresponding compression framework proposed by MPEG, i.e., geometry-based point cloud compression (G-PCC). In G-PCC, only the distance-based similarity is considered in the intra prediction for the attribute compression. In this paper, we propose a normal-based intra prediction scheme, which provides a more efficient lossless attribute compression by introducing the normals of point clouds. The angle between normals is used to further explore accurate local similarity, which optimizes the selection of predictors. We implement our method into the G-PCC reference software. Experimental results over LiDAR acquired datasets demonstrate that our proposed method is able to deliver better compression performance than the G-PCC anchor, with 2.1% gains on average for lossless attribute coding.

关键词： 3D point cloud G-PCC attribute compression normal-based prediction

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