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Efficient dynamic point cloud compression Through Adaptive Hierarchical Partitioning

IEEE ACCESS 2024年 12卷 152614-152629页

作者： Tohidi, Faranak Paul, Manoranjan Afsana, and Fariha Charles Sturt Univ Sch Comp Math & Engn Bathurst NSW 2795 Australia

Video-based point cloud compression (V-PCC) is a dynamic point cloud coding standard that compresses 3D point clouds by projecting them into 2D frames, involving computationally intensive steps that can hinder real-time applications in augmented and virtual reality. Several studies have endeavoured to mitigate this challenge by employing voxel classification and adaptive selection of voxels, yet the computationally intensive patch generation and refinement procedures persist. This paper presents an innovative approach that utilizes hierarchical hexahedron partitioning, adapting to varying point cloud densities and non-planar surfaces based on transmission requirements. The aim is to enhance projection by directly converting 3D data into 2D frames, bypassing segmentation and refinement. Crucially, the proposed method improves data capture by fine-tuning the projection layer of 2D frames, surpassing the capabilities of V-PCC's projection layer, and effectively addressing potential patch-boundary discontinuities. Furthermore, this approach enhances the efficiency of 2D frames by producing smaller maps than V-PCC. Experimental results demonstrate the efficacy of the proposed method by significantly reducing patch generation time in V-PCC while preserving quality and achieving significant bitrate savings. By employing the proposed method, it is achievable to attain approximately a 7.32% improvement in geometry BD-Rate (D1) and 6.61% (D2), along with a notable reduction in time complexity ranging from 33% to 59% based on high/low bitrate scenarios compared to the V-PCC anchor. This research thus tackles the pressing challenges of V-PCC by proposing a promising solution for the real-time application of dynamic point cloud compression.

关键词： point cloud compression Three-dimensional displays Time complexity Complexity theory Bit rate Transforms Indexes Geometry Standards Refining 3D compression complexity dynamic point cloud compression hexahedron partitioning

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Adaptive LPU Decision for dynamic point cloud compression

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 2370-2374页

作者： Mu, Xingming Gao, Wei Yuan, Hang Wang, Shunzhou Li, Ge Peking Univ Sch Elect & Comp Engn Shenzhen 518055 Peoples R China

With the rapid development of point cloud applications, dynamic point cloud compression has become a hot topic. A fast and accurate motion estimation scheme is the focus of dynamic point cloud compression, and the size of prediction units (PUs) affects the result of motion estimation. Improper size may even make the performance of inter-frame coding worse than that of intra-frame coding. However, the size of PUs is not fully explored. In this letter, we explore the impact of PUs size on inter-frame coding and propose an adaptive largest prediction unit (LPU) decision strategy. We first downsample original point clouds and obtain the features of adjacent frames. Then, the relationship between the optimal size of LPU and the features of adjacent frames is built. Finally, the optimal size of LPU is used to guide the inter-frame coding. Experimental results show that the better and faster coding performance is achieved by our algorithm, where the bitrate is saved by 2.48%, and the encoding time is saved by 33.80% for point cloud lossless geometry compression. Moreover, our method ensures that inter-frame coding performance of G-PCC is superior to intra-frame coding in all the sequences.

关键词： point cloud compression Encoding Codes Vectors dynamics Motion estimation Accuracy dynamic point cloud compression G-PCC LPU motion estimation

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Transform Domain Temporal Prediction for dynamic point cloud compression 25

Transform Domain Temporal Prediction for Dynamic Point Cloud...

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25th IEEE International Workshop on Multimedia Signal Processing (MMSP)

作者： Biswal, Monsij Sivakumar, Kruthika Koratti Lint, Ting-Lan Rose, Kenneth Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA Natl Taipei Univ Technol Dept Elect Engn Taipei 10608 Taiwan

ISBN: (纸本)9798350338935

With the advent of immersive multimedia technologies, interest in dynamic point cloud compression standards, such as video-based point cloud compression (V-PCC), has been steadily growing. At the core of V-PCC, conventional video codecs such as High Efficiency Video Coding are employed for encoding the geometry video, attribute video and the occupancy map. Standard video codecs perform temporal prediction using motion compensation by simple pixel-copying from the reference frame. However, conventional pixel-domain prediction does not exploit spatial correlation within the block. This is highly sub-optimal especially due to large homogeneous regions in the geometry video. In this paper, spatial decorrelation is first achieved via the Discrete Cosine Transform. Prediction is then performed in the transform domain, where the observed variation in temporal correlation across frequencies is exploited for a more accurate prediction. Design of the correlation filters required for this procedure poses challenges. The inherent instability in closed-loop design is tackled using the Asymptotic Closed Loop paradigm which uses an iterative open-loop algorithm that asymptotically converges to closed-loop operation. Experimental results on geometry videos generated from common test conditions show significant bitrate savings of up to 5.8% (about 3% on average) and highlight the efficacy of our approach.

关键词： dynamic point cloud compression HEVC transform-domain temporal prediction

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Patch Re-Segmentation and Packing for dynamic point cloud compression via Back-and-Forth Structure

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2022年 3卷 155-168页

作者： Shi, Haoyu Li, Fan Xi An Jiao Tong Univ Sch Informat & Commun Engn Shaanxi Key Lab Deep Space Explorat Intelligent I Xian 710049 Peoples R China

The dynamic point cloud is widely needed in 3D vision related applications such as virtual reality and telepresence. Due to the huge amount of data, a key technology before the effective application is the dynamic point cloud compression. The state-of-the-art dynamic point cloud compression scheme, video-based point cloud compression (V-PCC), generates 2D videos with some uncorrelation due to the patch segmentation and packing process, which will affect the compression efficiency. In this paper, we propose a Packing with Patch Correlation Improvement (PPCI) algorithm to adaptively remove the uncorrelated parts between matched patches in packing for the sake of inter-prediction performance. We first propose a basic unidirectional patch re-segmentation operator to remove the uncorrelated parts of the patches in the current point cloud relative to the patches in its reference point cloud. The removed parts will be formed as new patches and added to the patch collection of the current point cloud. Then we propose a back-and-forth structure, which is a combination of several basic patch re-segmentation operators, to bilaterally remove the uncorrelated parts of matched patches in a back-and-forth (BF) unit. Furthermore, we propose a framework to adaptively decide the best length of each BF unit in a point cloud sequence. Experimental results show that our method achieves noticeable bitrate savings compared with the existing V-PCC packing methods, particularly for sequences with small motion.

关键词： point cloud compression Videos Image coding Correlation Encoding Three-dimensional displays Motion estimation dynamic point cloud compression packing patch correlation video coding

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PKU-DPCC: A New Dataset for dynamic point cloud compression

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APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING 2024年第6期13卷

作者： Xie, Liang Mu, Xingming Li, Ge Gao, Wei Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

Due to the limitations of current dynamic point cloud compression (DPCC) datasets, such as scarce categories, lack of detailed textures, and minimal variation in point cloud sequence movements. We present a comprehensive and diverse dataset, namely PKU-DPCC, to address the need for more categories and scene diversity for DPCC. Compared with the existing Moving Picture Experts Group (MPEG) and Audio Video Coding Standard (AVS) PCC datasets, the proposed dataset shows significant superiority in data scale, diversity, and compression difficulty. Specifically, our PKU-DPCC encompasses 50 dynamic point cloud sequences of six superclasses, and each sequence consists of 250 frames with geometry and attribute information and embodies the object or scene of a specific subclasses in the real world. Besides the diverse data content, samples in our dataset possess precise geometry details and various motion patterns, catering to a wide range of testing requirements in dynamic PCC. To construct this new dataset, we first collect numerous 3D meshes and then check the quality of each sample, resulting in 50 high-quality sequences for conversion to our final point cloud dataset. Furthermore, we conduct precise annotations for two scenarios of perceptible distortions and quality assessments on the provided point cloud data, which aims to broaden the range of its applications. To facilitate a fast algorithm performance evaluation, we select a part of representative samples constituting a subset, which has been adopted to the AVS PCC dataset. We conduct lossless and lossy compression tests on both geometry and attribute information from our subset to demonstrate the necessity of our newly constructed dataset. Experimental results reveal that the proposed dataset can become a new benchmark for evaluating and improving dynamic PCC algorithms. Our dataset is publicly available at https://***/OpenDatasets/PKU-DPCC.

关键词： dynamic point cloud compression compression dataset K-means G-PCC codec AVS PCRM software

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Occupancy map-based low complexity motion prediction for video-based point cloud compression

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2024年 100卷

作者： Wang, Yihan Wang, Yongfang Cui, Tengyao Fang, Zhijun Shanghai Univ Shanghai Inst Adv Commun & Data Sci Sch Commun & Informat Engn Shanghai Peoples R China Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China Shanghai Univ Shanghai Inst Adv Commun & Data Sci Sch Commun & Informat Engn 99 Shangda Rd Shanghai 200444 Peoples R China Donghua Univ Sch Comp Sci & Technol 2999 Renmin North Rd Shanghai 201620 Peoples R China

This paper proposes an occupancy map -based low complexity motion prediction method for video -based point cloud compression (V-PCC). Firstly, we propose to utilize the occupancy map, direction gradient, and regional dispersion to divide the attribute maps into static, complex, and common blocks. Then, we propose an early termination method for static blocks, an adaptive motion search range method for complex blocks, and an early inter prediction mode decision algorithm for affine motion regions in common blocks. Experiment results show that, in comparison to the test model category2 (TMC2) v15.0, called the anchor method, the average bitrate savings of Y, U, and V components of the proposed method achieve 24.27%, 32.64%, and 31.23% on 8i voxelized full bodies version 2 (8iVFBv2) dataset, respectively. Further, the time savings is 41.97% for attribute maps. Similarly, the proposed method also achieves consistent performance on Microsoft voxelized upper bodies (MVUB) dataset.

关键词： dynamic point cloud compression Occupancy map Low complexity Motion prediction

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Unsupervised learning-based fast CU size decision for geometry videos in V-PCC

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2024年第1期21卷 11-11页

作者： Li, Yue Huang, Jun Wang, Chaofeng Huang, Hongyue Univ South China Sch Comp Sci Hengyang 421001 Peoples R China Peking Univ Natl Engn Res Ctr Visual Technol Sch Comp Sci Beijing 100871 Peoples R China

In video-based point cloud compression (V-PCC), the geometry video and attribute video are generated through the projection of the 3D dynamic point clouds (DPCs), which can be further encoded with the High-Efficiency Video Coding (HEVC) standard. However, due to the huge amount of data in the DPC, the coding complexity is extremely high when encoding the geometry and attribute videos with V-PCC. In this work, we focus on compression for geometry videos which is completely different from natural videos. As a new coding structure has been adopted to encode the geometry video with V-PCC under the All Intra (AI) configuration, the existing methods to reduce the coding complexity are not suitable for geometry videos. Hence, we propose a fast Coding Unit (CU) size decision method for geometry videos based on unsupervised learning. As the geometry video contains regions of sharp edges and different scales, we design a novel hierarchical clustering method to determine the optimal CU sizes for encoding. In addition, an adaptive linkage threshold is introduced in the clustering process according to different quantization parameters and CU sizes. Experimental results show that, under the AI configuration, the savings in the coding time for the geometry videos from different DPCs achieved by the proposed approach range from 56.7% to 69.3% on average while only 0.1-0.5% of increments in Bjontegaard delta total rate (BD-TotalRate) can be observed. Compared to various competing methods, the proposed approach achieves a better balance between coding complexity and encoding loss.

关键词： 3D point cloud dynamic point cloud compression CU size decision Hierarchical clustering Unsupervised learning

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A Transform Coding Strategy for dynamic point clouds

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 8213-8225页

作者： Milani, Simone Polo, Enrico Limuti, Simone Univ Padua Dept Informat Engn I-35131 Padua Italy

The development of real-time 3D sensing devices and algorithms (e.g., multiview capturing systems, Time-of-Flight depth cameras, LIDAR sensors), as well as the widespreading of enhanced user applications processing 3D data, have motivated the investigation of innovative and effective coding strategies for 3D point clouds. Several compression algorithms, as well as some standardization efforts, has been proposed in order to achieve high compression ratios and flexibility at a reasonable computational cost. This paper presents a transform-based coding strategy for dynamic point clouds that combines a non-linear transform for geometric data with a linear transform for color data;both operations are region-adaptive in order to fit the characteristics of the input 3D data. Temporal redundancy is exploited both in the adaptation of the designed transform and in predicting the attributes at the current instant from the previous ones. Experimental results showed that the proposed solution obtained a significant bit rate reduction in lossless geometry coding and an improved rate-distortion performance in the lossy coding of color components with respect to state-of-the-art strategies.

关键词： Three-dimensional displays Image coding Transforms Solid modeling Image color analysis Sensors Heuristic algorithms dynamic point cloud compression cellular automata transform coding octree voxel color

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A TRANSFORM CODING STRATEGY FOR VOXELIZED dynamic point cloudS 25

A TRANSFORM CODING STRATEGY FOR VOXELIZED DYNAMIC POINT CLOU...

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25th IEEE International Conference on Image Processing (ICIP)

作者： Limuti, Simone Polo, Enrico Milani, Simone Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9781479970612

With the advent of virtual and augmented reality applications, 3D and free-viewpoint representations have evolved towards solid scene models using meshes and point clouds. Recent works have been addressing point clouds compression via octree-based hierarchical strategies in order to enable a multi-resolution coding and visualization at a reasonable computational cost. This paper presents a voxelized dynamic point cloud coding scheme that combines a Cellular Automata block reversible transform for geometric data with a region adaptive transform for color data. Temporal redundancy is removed using a low-complexity prediction scheme to minimize the computational complexity and reduce the coded bit rate. Experimental results showed that the proposed solution obtained a significant bit rate reduction in lossless geometry coding and an improved rate-distortion performance in the lossy coding of color components with respect to state-of-the-art strategies.

关键词： dynamic point cloud compression cellular automata transform coding octree voxel color

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