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H-PCC: point cloud compression With Hybrid Mode Selection and Content Adaptive Down-Sampling

IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第4期10卷 3054-3061页

作者： Liu, Bowen Chen, Yu Wang, Boyang Yang, Mingyu Kim, Hun-Seok Univ Michigan EECS Dept Ann Arbor MI 48109 USA

LiDAR sensors are integral to autonomous driving and augmented reality applications, providing essential depth information. However, managing the substantial volume of LiDAR point cloud data is crucial for practical application, necessitating efficient compression algorithms. Similar to other data compression domains, point cloud compression aims to spatially and temporally decorrelate information while preserving reconstruction quality. Addressing real-world challenges such as sensor pose retrieval inaccuracies or motion estimation failures, we introduce H-PCC, a hybrid LiDAR data compression pipeline that combines a range image prediction-based dynamic compression method with a novel point clustering octree-based static method. Additionally, we demonstrate that our content adaptive down-sampling technique using associated camera images can significantly enhance the LiDAR data compression rate with minimal impact on the accuracy of machine perception tasks. Extensive evaluations on the KITTI and Oxford datasets, using both traditional perceptual quality metrics and machine vision tasks, show that H-PCC surpasses state-of-the-art LiDAR data compression methods.

关键词： point cloud compression Laser radar Image coding Three-dimensional displays Data compression Sensors Measurement by laser beam Octrees Entropy Image reconstruction Computer vision for automation deep learning for visual perception sensor fusion

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suLPCC: A Novel LiDAR point cloud compression Framework for Scene Understanding Tasks

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第5期21卷 3816-3827页

作者： Wang, Miaohui Huang, Runnan Liu, Ye Li, Yanshan Xie, Wuyuan Shenzhen Univ SZU Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Nanjing 210003 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

Light detection and ranging (LiDAR) point cloud compression (LPCC) plays an important role in managing the storage, transmission, and perception of the rapidly expanding volume of LiDAR point cloud (LPC) data. However, there has been a noticeable lack of comprehensive investigation into LPCC methods specifically designed for environmental perception and understanding. To address this gap, we propose a new LPCC framework aimed at meeting the unique requirements of various scene understanding tasks, enhancing the adaptability of LPCCs in real-world scenarios. Specifically, we divide the input LPCs into an object and a scene component through a distinction module, design a new point completion-based method to encode object LPCs, and develop novel structure-aware intracoding and motion-optimized intercoding schemes to compress scene LPCs. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method on the localization, mapping, and detection tasks. We believe that the findings presented in this article will contribute to a deeper understanding of LPCCs as well as promote further development of LiDAR sensor-based systems.

关键词： point cloud compression Laser radar Autoencoders Image coding Entropy Accuracy Octrees Location awareness Transform coding Three-dimensional displays Light detection and ranging (LiDAR) point cloud (LPC) object compression scene compression scene understanding task-driven compression

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Overview and Comparison of AVS point cloud compression Standard

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

作者： Gao, Wei Gao, Wenxu Mu, Xingming Peng, Changhao Li, Ge Peking Univ Sch Elect & Comp Engn Guangdong Prov Key Lab Ultra High Definit Immers M Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

point cloud is a prevalent 3D data representation format with significant application values in immersive media, autonomous driving, digital heritage protection, etc. However, the large data size of point clouds poses challenges to transmission and storage, which influences the wide deployments. Therefore, point cloud compression plays a crucial role in practical applications for both human and machine perception optimization. To this end, the Moving Picture Experts Group (MPEG) has established two standards for point cloud compression, including Geometry-based point cloud compression (G-PCC) and Video-based point cloud compression (V-PCC). In the meantime, the Audio Video coding Standard (AVS) Workgroup of China also have launched and completed the development for its first generation point cloud compression standard, namely AVS PCC. This new standardization effort has adopted many new coding tools and techniques, which are different from the other counterpart standards. This paper reviews the AVS PCC standard from two perspectives, i.e., the related technologies and performance comparisons.

关键词： point cloud compression Rate-distortion performance Geometry and attribute coding AVS and MPEG standards

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TSC-PCAC: Voxel Transformer and Sparse Convolution-Based point cloud Attribute compression for 3D Broadcasting

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IEEE TRANSACTIONS ON BROADCASTING 2025年第1期71卷 154-166页

作者： Guo, Zixi Zhang, Yun Zhu, Linwei Wang, Hanli Jiang, Gangyi Sun Yat sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen 518017 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 200092 Peoples R China Ningbo Univ Fac Informat & Sci & Engn Ningbo 315211 Peoples R China

point cloud has been the mainstream representation for advanced 3D applications, such as virtual reality and augmented reality. However, the massive data amounts of point clouds is one of the most challenging issues for transmission and storage. In this paper, we propose an end-to-end voxel Transformer and Sparse Convolution based point cloud Attribute compression (TSC-PCAC) for 3D broadcasting. Firstly, we present a framework of the TSC-PCAC, which includes Transformer and Sparse Convolutional Module (TSCM) based variational autoencoder and channel context module. Secondly, we propose a two-stage TSCM, where the first stage focuses on modeling local dependencies and feature representations of the point clouds, and the second stage captures global features through spatial and channel pooling encompassing larger receptive fields. This module effectively extracts global and local inter-point relevance to reduce informational redundancy. Thirdly, we design a TSCM based channel context module to exploit inter-channel correlations, which improves the predicted probability distribution of quantized latent representations and thus reduces the bitrate. Experimental results indicate that the proposed TSC-PCAC method achieves an average of 38.53%, 21.30%, and 11.19% bitrate reductions on datasets 8iVFB, Owlii, 8iVSLF, Volograms, and MVUB compared to the Sparse-PCAC, NF-PCAC, and G-PCC v23 methods, respectively. The encoding/decoding time costs are reduced 97.68%/98.78% on average compared to the Sparse-PCAC. The source code and the trained TSC-PCAC models are available at https://***/igizuxo/TSC-PCAC.

关键词： point cloud compression Image coding Convolution Three-dimensional displays Geometry Transforms Transformers voxel transformer sparse convolution variational autoencoder channel context module

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Scalable point cloud Attribute compression

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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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Discrete Fourier Transform-Based point cloud compression for Efficient SLAM in Featureless Terrain

Discrete Fourier Transform-Based Point Cloud Compression for...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Riku Suzuki Ayumi Umemura Shreya Santra Kentaro Uno Kazuya Yoshida Department of Aerospace Engineering Space Robotics Lab. (SRL) Tohoku University Sendai Japan

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Simultaneous Localization and Mapping (SLAM) is an essential technology for the efficiency and reliability of unmanned robotic exploration missions. While the onboard computational capability and communication bandwidth are critically limited, the point cloud data handled by SLAM is large in size, attracting attention to data compression methods. To address such a problem, in this paper, we propose a new method for compressing point cloud maps by exploiting the Discrete Fourier Transform (DFT). The proposed technique converts the Digital Elevation Model (DEM) to the frequency-domain 2D image and omits its high-frequency components, focusing on the exploration of gradual terrains such as planets and deserts. Unlike terrains with detailed structures such as artificial environments, high-frequency components contribute little to the representation of gradual terrains. Thus, this method is effective in compressing data size without significant degradation of the point cloud. We evaluated the method in terms of compression rate and accuracy using camera sequences of two terrains with different elevation profiles.

关键词： point cloud compression Simultaneous localization and mapping Image coding Discrete Fourier transforms Moon Focusing Real-time systems Deserts Reliability Mobile robots

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MP-DPCC: A Motion Proxy-Based Dynamic point cloud compression Framework

MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compressio...

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

作者： ZhaoYi Jiang Dong Han Cao Song FangZhe Nan Bailin Yang School of Computer Science and Technolgy Zhejiang Gongshang University School of Statistics and Mathematics Zhejiang Gongshang University Economic Forecasting and Policy Simulation Laboratory Zhejiang Gongshang University Collaborative Innovation Center of Statistical Data Engineering Technology & Application Zhejiang Gongshang University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The increasing data volume and the demand for real-time transmission highlight the necessity for efficient compression of dynamic point cloud data. Existing methods primarily focus on reducing inter-frame redundancy by calculating per-point motion information, overlooking the computational and storage costs involved. In this paper, we propose a novel motion proxy-based dynamic point cloud compression framework to enhance the efficiency and accuracy of motion information utilization. Specifically, we introduce a feature proxy module to adaptively locate proxy points, which represent the overall motion through the motion of proxy points. Additionally, a motion enhancement module is employed to refine motion details and prevent local information loss caused by dense motion trajectories. Extensive experiments demonstrate the superiority of our approach. Compared with baseline methods, it achieves an average BD-rate improvement of 12.07% (D1) and 11.90% (D2).

关键词： point cloud compression Costs Accuracy Dynamics Termination of employment Signal processing Real-time systems Acoustics Trajectory Speech processing

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Adaptive Learning-Based Bilateral Filtering for Voxel Recoloring in point cloud compression

Adaptive Learning-Based Bilateral Filtering for Voxel Recolo...

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IEEE Southeastcon

作者： Dewan Fahim Noor Sadia Sikder ECE Department Tuskegee University Tuskegee AL USA Department of Chemical Engineering Auburn University Auburn AL USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

In point cloud compression, voxels need to be recolored while preserving the maximum quality of experience (QoE) possible. Current point cloud recoloring techniques usually assign the average color intensity of all points within a voxel to its centroid. However, this approach neglects the spatial distribution of points, which is crucial for preserving visual fidelity. In our paper, we propose a learning based bilateral filtering technique on point cloud data. The geometry and photometric features of the local voxel region are computed to predict optimal kernel sizes in the bilateral filters to achieve adaptive performance. Getting motivated from the guided filter, a learning-based network is implemented to learn the optimal kernel sizes by training the variance and the other attributes-features of the points. Simulation results demonstrate modest gains, and the scheme holds significant potential for future optimization.

关键词： point cloud compression Training Visualization Filters Graphical models Image color analysis Simulation Quality of experience Kernel Optimization

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Cross-Component Residual Prediction for Geometry-Based point cloud compression

Cross-Component Residual Prediction for Geometry-Based Point...

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

作者： Bharath Vishwanath Yingzhan Xu Kai Zhang Li Zhang Bytedance Inc. San Diego CA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

point cloud compression is pivotal for the success of immersive multimedia applications. For attribute compression in geometry-based point cloud compression (G-PCC), Region Adaptive Hierarchical Transform (RAHT) is the preferred coding method. Inspired by the significant impact of cross-component prediction in traditional image and video coding, we investigate and present our pioneering work on cross-component residual prediction for RAHT in G-PCC. The method builds on the core observation that cross-component correlations are observed locally in some regions in some sequences. Accordingly, the prediction is employed for last few layers of RAHT which capture local characteristics. We employ a simple linear model, that predicts chroma residues from reconstructed luma residue. The prediction coefficients are learnt on the fly from reconstructed residues of the neighbors. The method gives 1% luma coding gain and around 2-3% chroma coding gain with negligible increase in complexity. The method is adopted to the Geometric Solid Test Model (GeS-TM v7.0), a dedicated codec being developed for solid point clouds.

关键词： point cloud compression Video coding Correlation Transforms Streaming media Solids Encoding Complexity theory Speech processing Image reconstruction

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point cloud compression with Implicit Neural Representations: A Unified Framework

Point Cloud Compression with Implicit Neural Representations...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Ruan, Hongning Shao, Yulin Yang, Qianqian Zhao, Liang Niyato, Dusit Zhejiang Univ Dept Informat Sci ence & Elect Engn Hangzhou Peoples R China Univ Macau State Key Lab Internet Things Smart City Taipa Macau Peoples R China Univ Macau Dept Elect & Comp Engn Taipa Macau Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9798350378412

point clouds have become increasingly vital across various applications thanks to their ability to realistically depict 3D objects and scenes. Nevertheless, effectively compressing unstructured, high-precision point cloud data remains a significant challenge. In this paper, we present a pioneering point cloud compression framework capable of handling both geometry and attribute components. Unlike traditional approaches and existing learning-based methods, our framework utilizes two coordinate-based neural networks to implicitly represent a voxelized point cloud. The first network generates the occupancy status of a voxel, while the second network determines the attributes of an occupied voxel. To tackle an immense number of voxels within the volumetric space, we partition the space into smaller cubes and focus solely on voxels within non-empty cubes. By feeding the coordinates of these voxels into the respective networks, we reconstruct the geometry and attribute components of the original point cloud. The neural network parameters are further quantized and compressed. Experimental results underscore the superior performance of our proposed method compared to the octree-based approach employed in the latest G-PCC standards. Moreover, our method exhibits high universality when contrasted with existing learning-based techniques.

关键词： point cloud compression implicit neural representation neural network compression

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