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3D point cloud attribute compression Using Diffusion-Based Texture-Aware Intra Prediction

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第10期34卷 9633-9646页

作者： Shao, Yiting Yang, Xiaodong Gao, Wei Liu, Shan Li, Ge Peking Univ Sch Elect & Comp Engn SECE Shenzhen Grad Sch Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Peking Univ Shenzhen Grad Sch SECE Shenzhen 518055 Peoples R China Tencent Media Lab Palo Alto CA 94306 USA

There is an urgent need from various multimedia applications to efficiently compress point clouds. The Moving Picture Experts Group has released a standard platform called geometry-based point cloud compression (G-PCC). However, its k-nearest neighbor (k-NN) based attribute prediction has limited efficiency for point clouds with rich texture and directional information. To overcome this problem, we propose a texture-aware attribute predictive coding framework in a point cloud diffusion model. In our work, attribute intra prediction is solved as a diffusion-based interpolation problem, and a general attribute predictor is developed. It is theoretically proven that G-PCC k-NN based predictor is a degraded case of the proposed diffusion-based solution. First, a point cloud is represented as two levels of details with seeds as the inpainting mask and non-seed points to be predicted. Second, we design point cloud partial difference operators to perform energy-minimizing attribute inpainting from seeds to unknowns. Smooth attribute interpolation can be achieved via an iterative diffusion process, and an adaptive early termination is proposed to reduce complexity. Third, we propose a structure-adaptive attribute predictive coding scheme, where edge-enhancing anisotropic diffusion is employed to perform texture-aware attribute prediction. Finally, attributes of seeds are beforehand encoded and prediction residuals of left points are progressively encoded into bitstream. Experiments show the proposed scheme surpasses the state-of-the-art by an average of 14.14%, 17.52%, and 17.87% BD-BR gains on the coding of Y, U, and V components, respectively. Subjective results on attribute reconstruction quality also verify the advantage of our scheme.

关键词： point cloud compression Image coding Codecs Three-dimensional displays Interpolation Encoding Correlation point cloud attribute compression progressive intra prediction diffusion-based interpolation predictive coding

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Efficient Multi-stage Context Based Entropy Model for Learned Lossy point cloud attribute compression

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APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING 2025年第2期14卷

作者： Wang, Kai Zhang, Pingping Jiao, Shengjie Yuan, Hui Wang, Shiqi Wang, Xu Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China

The autoregressive entropy model facilitates high compression efficiency by capturing intricate dependencies but suffers from slow decoding due to its serial context dependencies. To address this, we propose ParaPCAC, a lossy Parallel point cloud attribute compression scheme, designed to optimize the efficiency of the autoregressive entropy model. Our approach focuses on two main components: a parallel decoding strategy and a multi-stage context-based entropy model. In the parallel decoding strategy, we partition the voxels of the quantized latent features into non-overlapping groups for independent context entropy modeling, enabling parallel processing. The multi-stage context based entropy model is employed to decode neighboring features concurrently, utilizing previously decoded features at each stage. Global hyperprior is incorporated after the first stage to improve the estimation of attribute probability. Through these two techniques, ParaPCAC achieves significant decoding speed enhancements, with an acceleration of up to 160x and a 24.15% BD-Rate reduction compared to serial autoregressive entropy models. Furthermore, experimental results demonstrate that ParaPCAC outperforms existing learning-based methods in rate-distortion performance and decoding latency.

关键词： point cloud compression point cloud attribute compression learned data compression

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EFFICIENT point cloud attribute compression USING RICH PARALLELIZABLE CONTEXT MODEL 49

EFFICIENT POINT CLOUD ATTRIBUTE COMPRESSION USING RICH PARAL...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huang, Ruishan Yu, Pengpeng Li, Shaolin Liang, Fan Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Guangdong Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350344868;9798350344851

The autoregressive context model has been proven effective in point cloud attribute compression. However, it suffers from unbearable decoding latency due to the limitations of serial decoding and the large scale of point clouds. In this paper, we propose a rich, parallelizable context model for point cloud attribute compression to speed up the decoding process. To further improve rate-distortion (RD) performance, we propose cross-coordinate and intra-coordinate attention modules to reduce the spatial redundancy of the latent representations. We validate our method on the large-scale Moving Picture Experts Group (MPEG) point cloud benchmarks, and demonstrate that our model achieves much lower decoding time than previous autoregression-based methods while maintaining similar RD performance.

关键词： point cloud attribute compression sparse representation parallelizable context model

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A efficient predictive wavelet transform for LiDAR point cloud attribute compression

A efficient predictive wavelet transform for LiDAR point clo...

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IEEE International Conference on Visual Communications and Image Processing (VCIP)

作者： Chen, Yueru Wang, Jing Li, Ge Peng Cheng Lab Shenzhen Peoples R China Peking Univ Shenzhen Grad Sch Beijing Peoples R China

ISBN: (纸本)9781665475921

In this paper, a new predictive wavelet transform (PWT) is proposed to solve LiDAR point clouds attribute compression. Our method is a combination of predictive coding and Haar wavelet transform. Based on the spatial information, a hierarchical predictive transform tree is designed to represent 3D irregular data points efficiently. Each level node is classified as a predictive node (P-node) or a transform node (T-node) according to the distances to its adjacent nodes. Then in a top-down coding process, the Haar transform is applied to all T-node pairs, and predictive coding is processed on all P-nodes alternately. It is shown by experimental results that the proposed PWT method offers better R-D performances compared with state-of-the-art methods.

关键词： point cloud attribute compression LiDAR Predictive coding Predictive wavelet transform

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attribute Artifacts Removal for Geometry-Based point cloud compression

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 3399-3413页

作者： Sheng, Xihua Li, Li Liu, Dong Xiong, Zhiwei Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Peoples R China

Geometry-based point cloud compression (G-PCC) can achieve remarkable compression efficiency for point clouds. However, it still leads to serious attribute compression artifacts, especially under low bitrate scenarios. In this paper, we propose a Multi-Scale Graph Attention Network (MS-GAT) to remove the artifacts of point cloud attributes compressed by G-PCC. We first construct a graph based on point cloud geometry coordinates and then use the Chebyshev graph convolutions to extract features of point cloud attributes. Considering that one point may be correlated with points both near and far away from it, we propose a multi-scale scheme to capture the short- and long-range correlations between the current point and its neighboring and distant points. To address the problem that various points may have different degrees of artifacts caused by adaptive quantization, we introduce the quantization step per point as an extra input to the proposed network. We also incorporate a weighted graph attentional layer into the network to pay special attention to the points with more attribute artifacts. To the best of our knowledge, this is the first attribute artifacts removal method for G-PCC. We validate the effectiveness of our method over various point clouds. Objective comparison results show that our proposed method achieves an average of 9.74% BD-rate reduction compared with Predlift and 10.13% BD-rate reduction compared with RAHT. Subjective comparison results present that visual artifacts such as color shifting, blurring, and quantization noise are reduced.

关键词： point cloud compression Image coding Quantization (signal) Convolutional neural networks Videos Task analysis Image color analysis compression artifacts removal geometry-based point cloud compression graph attention network graph convolution point cloud attribute compression

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Learning-based Intra-Prediction For point cloud attribute Transform Coding 24

Learning-based Intra-Prediction For Point Cloud Attribute Tr...

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IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)

作者： Hou, Lizhi Gao, Linyao Xu, Yiling Li, Zhu Xu, Xiaozhong Liu, Shan Shanghai Jiao Tong Univ Cooperat MediaNet Innovat Ctr Shanghai Peoples R China Univ Missouri Kansas Dept CSEE Kansas City KS USA Tencent Media Lab Shenzhen Peoples R China

ISBN: (数字)9781665471893

ISBN: (纸本)9781665471893

The attached attributes of each point in point cloud are fairly valuable but aggravate the burden for storage and transmission. In this paper, we propose a learning-based introprediction method for region adaptive hierarchical transform (RAHT) to compress point cloud attributes efficiently. First, we design an adaptive neighbor selection (ANS) module to produce the most correlated neighbors for child nodes. Then, the correlated neighbors obtained by ANS, the corresponding distance weights, and some additional auxiliary information are concatenated and fed to a multi-layer perception (MLP) based network to estimate the child node attributes precisely. Besides, residual learning is introduced to accelerate the network convergence. The predicted child node attributes are finally transformed by RAHT, and the residual of transform coefficients are then quantized and entropy coded. Experimental results demonstrate that our proposed methods can significantly improve attribute coding efficiency with average 10.2% BD-Rate gains compared with MPEG G-PCC reference software TMC13v14.0 on MPEG PCC dataset.

关键词： point cloud attribute compression region adaptive hierarchical transform intra-prediction MLP-based neural network

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point clouds attribute compression Using Data-Adaptive Intra prediction 33

Point Clouds Attribute Compression Using Data-Adaptive Intra...

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33rd IEEE International Conference on Visual Communications and Image Processing (IEEE VCIP)

作者： Zhang, Qi Shao, Yiting Li, Ge Peking Univ Shenzhen Grad Sch Sch Elect & Comp Engn Beijing Peoples R China

ISBN: (纸本)9781538644584

In recent years, 3D sensing and capture technologies have made constant progress, leading to point clouds with higher resolution and fidelity. Since most applications demand compact storage and fast transmission, the issue of how to compress point clouds efficiently becomes an intractable problem. While previous GFT-based solutions use the transform tool to decorrelate attributes directly, ignoring the overall attribute's data spatial redundancy, Graph Fourier Transform (GFT) has shown good performance on point cloud attribute compression. So, motivated by coding tools in traditional image and video coding, we propose a block-based data-adaptive intra prediction tool before graph transform processing to further reduce the redundancy. We adopt uniform quantizing and context-based arithmetic coding to get the final bitstream. Experimental results on different datasets demonstrate that our method improves the compression efficiency of other GFT-based schemes and has much better BD-BR performance than the state-of-the-art Region-Adaptive Hierarchical Transform (RAHT) approach on most specified point cloud contents.

关键词： point cloud attribute compression Data-adaptive Intra prediction Graph transform Entropy coding

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