计算机辅助设计与图形学学报

动态点云压缩技术的研究发展与综述

PDF下载

杨柏林 ¹⁾ , 应瑞琦 ¹⁾ , 丁丹丹 ²⁾ , 南方哲 ³⁾ , 赵俊杰 ⁴⁾ , 郑东 ⁵⁾

计算机辅助设计与图形学学报 | 三维图形计算 2025,37(12): 2033-2051

收起

计算机辅助设计与图形学学报 |三维图形计算 2025 , 37 (12) : 2033 -2051

动态点云压缩技术的研究发展与综述

全屏

杨柏林¹⁾, 应瑞琦¹⁾, 丁丹丹²⁾, 南方哲³⁾, 赵俊杰⁴⁾, 郑东⁵⁾

作者信息

¹⁾浙江工商大学计算机科学与技术学院　杭州　310018

²⁾杭州师范大学信息科学与技术学院　杭州　311121

³⁾浙江工商大学统计与数学学院　杭州　310018

⁴⁾中国移动（杭州）信息技术有限公司　杭州　310023

⁵⁾杭州宇泛智能科技股份有限公司　杭州　311121

通讯作者:

（ybl@zjgsu.edu.cn）

作者简介:

杨柏林（1975—），男，博士，教授，博士生导师，CCF会员，主要研究方向为数字媒体、大数据可视分析、虚拟/增强现实、人工智能

应瑞琦（2001—），男，硕士研究生，主要研究方向为动态点云压缩

丁丹丹（1983—），女，博士，副教授，硕士生导师，CCF会员，主要研究方向为计算机视觉、智能视频图像处理、智能视频编码、三维点云压缩编码与重建

南方哲（1994—），女，博士研究生，CCF会员，主要研究方向为动态点云压缩

赵俊杰（1985—），男，硕士，高级工程师，主要研究方向为视联网

郑东（1990—），男，博士研究生，高级工程师，主要研究方向为智能物联.

Research Progress and Review of Dynamic Point Cloud Compression Technology

Bailin Yang¹⁾, Ruiqi Ying¹⁾, Dandan Ding²⁾, Fangzhe Nan³⁾, Junjie Zhao⁴⁾, Dong Zheng⁵⁾

Affiliations

¹⁾School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou　310018

²⁾School of Information Science and Technology, Hangzhou Normal University, Hangzhou　311121

³⁾School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou　310018

⁴⁾China Mobile (Hangzhou) Information Technology Company Limited, Hangzhou　310023

⁵⁾Universal Ubiquitous Co., Ltd., Hangzhou　311121

出版时间: 2025-12-20 doi: 10.3724/SP.J.1089.2024-00699

文章导航

摘要

收起

动态点云数据因具备时间连续性和高维数据特性，能准确地描述物体动态变化，广泛应用于自动驾驶、增强现实、虚拟现实、机器人导航和三维视频等. 动态点云压缩在有效地管理不断增长的点云数据量的存储、传输和感知方面发挥着重要作用，当前缺乏专门针对动态点云压缩方法的全面调查. 针对该问题，对动态点云压缩方法进行了系统的综述. 首先介绍动态点云压缩的研究意义和理论支撑；然后从几何压缩、属性压缩和联合压缩3个方面阐述了主流模型的基本原理及优势；之后介绍了主流的动态点云压缩数据集及评价指标，总结各种方法在不同数据集上的性能；最后分析了当前方法在算法复杂度与场景适应性方面的局限性，并指出轻量化网络设计、联合时空建模及跨模态信息融合是未来的主要研究方向. 文中总结了动态点云压缩的最新进展，有助于更深入理解动态点云压缩，促进基于动态点云压缩系统的进一步发展.

关键词

动态点云压缩 / 深度学习 / 几何压缩 / 属性压缩 / 联合压缩

Abstract

收起

Dynamic point cloud data, characterized by temporal continuity and high-dimensional features, can accurately capture dynamic changes of objects and are thus widely applied in fields such as autonomous driving, augmented reality, virtual reality, robotic navigation, and 3D video. Dynamic point cloud compression plays a crucial role in efficiently managing the storage, transmission, and perception of the ever-increasing volume of point cloud data. Despite this demand, there remains a clear lack of comprehensive surveys specifically focused on dynamic point cloud compression methods. To address this gap, this paper presents a systematic review of existing approaches, aiming to summarize recent advancements in the field and provide insights for future research. The paper first introduces the significance and theoretical foundations of dynamic point cloud compression. It then elaborates on the fundamental principles and advantages of mainstream models from three perspectives: geometry compression, attribute compression, and joint compression. Subsequently, commonly used datasets and evaluation metrics are reviewed, and the performance of various methods across different datasets is summarized. Finally, the paper analyzes the limitations of current methods in terms of algorithmic complexity and scene adaptability, and points out that lightweight network design, joint spatiotemporal modeling, and cross-modal information fusion are major directions for future research. This survey is expected to facilitate a deeper understanding of dynamic point cloud compression and promote further development of related systems.

Key words

dynamic point cloud compression / deep learning / geometric compression / attribute compression / joint compression

引用本文

杨柏林, 应瑞琦, 丁丹丹, 南方哲, 赵俊杰, 郑东. 动态点云压缩技术的研究发展与综述. 计算机辅助设计与图形学学报, 2025 , 37 (12) : 2033 -2051 . DOI: 10.3724/SP.J.1089.2024-00699

Bailin Yang, Ruiqi Ying, Dandan Ding, Fangzhe Nan, Junjie Zhao, Dong Zheng. Research Progress and Review of Dynamic Point Cloud Compression Technology[J]. Journal of Computer-Aided Design & Computer Graphics, 2025 , 37 (12) : 2033 -2051 . DOI: 10.3724/SP.J.1089.2024-00699

基金

收起

国家自然科学基金(62172366)
浙江省“尖兵”“领雁”研发攻关计划(2023C01150)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

[1]

Alliez P. Recent advances in compression of 3D meshes[C]//Proceedings of the 13th European Signal Processing Conference. Los Alamitos: IEEE Computer Society Press, 2005: 1-4

[2]

Aleksandrov M, Zlatanova S, Heslop D J. Voxelisation algorithms and data structures: a review[J]. Sensors, 2021, 21(24): Article No.8241

[3]

Garcia D C, de Queiroz R L. Intra-frame context-based octree coding for point-cloud geometry[C]//Proceedings of the 25th IEEE International Conference on Image Processing. Los Alamitos: IEEE Computer Society Press, 2018: 1807-1811

[4]

Jiang Zhaoyi, Zou Wenqin, Song Chao, et al. Variable rate compression of point cloud based on channel gain[J]. Journal of Computer-Aided Design & Computer Graphics, 2024, 36(11): 1816-1824 (in Chinese) (江照意, 邹文钦, 宋超, 等. 基于通道增益的可变比特率点云压缩[J]. 计算机辅助设计与图形学学报, 2024, 36(11): 1816-1824)

[5]

Akhtar A, Gao W, Zhang X, et al. Point cloud geometry prediction across spatial scale using deep learning[C]//Proceedings of the IEEE International Conference on Visual Communications and Image Processing. Los Alamitos: IEEE Computer Society Press, 2020: 70-73

[6]

Malvar H S. Adaptive run-length/Golomb-Rice encoding of quantized generalized Gaussian sources with unknown statistics[C]//Proceedings of the Data Compression Conference. Los Alamitos: IEEE Computer Society Press, 2006: 23-32

[7]

Jiang Z Y, Ding L Y, Tam G K L, et al. C2SPoint: a classification-to-saliency network for point cloud saliency detection[J]. Computers & Graphics, 2023, 115: 274-284

[8]

Yan W, Shao Y T, Liu S, et al. Deep AutoEncoder-based lossy geometry compression for point clouds[OL]. [2024-11-16]. https://arxiv.org/abs/1905.03691

[9]

Wiesmann L, Milioto A, Chen X Y L, et al. Deep compression for dense point cloud maps[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 2060-2067

[10]

Yamaguchi K, Kunii T L, Fujimura K, et al. Octree-related data structures and algorithms[J]. IEEE Computer Graphics and Applications, 1984, 4(1): 53-59

[11]

Meagher D. Geometric modeling using octree encoding[J]. Computer Graphics and Image Processing, 1982, 19(2): 129-147

[12]

Wu C Y, Singhal N, Krähenbühl P. Video compression through image interpolation[C]//Proceedings of the 15th European Conference on Computer Vision. Heidelberg: Springer, 2018: 425-440

[13]

Yang R H, Yang Y B, Marino J, et al. Hierarchical autoregressive modeling for neural video compression[OL]. [2024-11-16]. https://arxiv.org/abs/2010.10258

[14]

Hu Z H, Lu G, Xu D. FVC: a new framework towards deep video compression in feature space[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2021: 1502-1511

[15]

Ballé J, Laparra V, Simoncelli E P. End-to-end optimized image compression[OL]. [2024-11-16]. https://arxiv.org/abs/1611.01704v3

[16]

Ballé J, Minnen D, Singh S, et al. Variational image compression with a scale hyperprior[OL]. [2024-11-16]. https://arxiv.org/abs/1802.01436v2

[17]

Bross B, Chen J L, Ohm J R, et al. Developments in international video coding standardization after AVC, with an overview of versatile video coding (VVC)[J]. Proceedings of the IEEE, 2021, 109(9): 1463-1493

[18]

Schwarz H, Marpe D, Wiegand T. Overview of the scalable video coding extension of the H.264/AVC standard[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2007, 17(9): 1103-1120

[19]

Sullivan G J, Ohm J R, Han W J, et al. Overview of the high efficiency video coding (HEVC) standard[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2012, 22(12): 1649-1668

[20]

Djelouah A, Campos J, Schaub-Meyer S, et al. Neural inter-frame compression for video coding[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, 2019: 6420-6428

[21]

Lu G, Ouyang W L, Xu D, et al. DVC: an end-to-end deep video compression framework[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2019: 10998-11007

[22]

Nan F Z, Li F, Wang Z Y, et al. Multi-modal dynamic point cloud geometric compression based on bidirectional recurrent scene flow[C]//Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Los Alamitos: IEEE Computer Society Press, 2025: 1-5

[23]

Fu C Y, Li G, Song R, et al. OctAttention: octree-based large-scale contexts model for point cloud compression[C]//Proceedings of the 36th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2022: 625-633

[24]

Quach M, Valenzise G, Dufaux F. Learning convolutional transforms for lossy point cloud geometry compression[C]//Proceedings of the IEEE International Conference on Image Processing. Los Alamitos: IEEE Computer Society Press, 2019: 4320-4324

[25]

Choy C, Gwak J Y, Savarese S. 4D spatio-temporal ConvNets: minkowski convolutional neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2019: 3070-3079

[26]

Jiang Z Y, Han D, Song C, et al. MP-DPCC: a motion proxy-based dynamic point cloud compression framework[C]//Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Los Alamitos: IEEE Computer Society Press, 2025: 1-5

[27]

de Oliveira Rente P, Brites C, Ascenso J, et al. Graph-based static 3D point clouds geometry coding[J]. IEEE Transactions on Multimedia, 2019, 21(2): 284-299

[28]

Nguyen D T, Quach M, Valenzise G, et al. Learning-based lossless compression of 3D point cloud geometry[C]//Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Los Alamitos: IEEE Computer Society Press, 2021: 4220-4224

[29]

Schnabel R, Klein R. Octree-based point-cloud compression[C]//Proceedings of the 3rd Eurographics/IEEE VGTC conference on Point-Based Graphics. Aire-la-Ville: Eurographics Association Press, 2006: 111-121

[30]

Huang L L, Wang S L, Wong K, et al. Octsqueeze: octree-structured entropy model for liDAR compression[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2020: 1310-1320

[31]

Zhang C, Florêncio D, Loop C. Point cloud attribute compression with graph transform[C]//Proceedings of the IEEE International Conference on Image Processing. Los Alamitos: IEEE Computer Society Press, 2014: 2066-2070

[32]

Slomp M, Kawasaki H, Furukawa R, et al. Temporal octrees for compressing dynamic point cloud streams[C]//Proceedings of the 2nd International Conference on 3D Vision. Los Alamitos: IEEE Computer Society Press, 2014: 49-56

[33]

Kammerl J, Blodow N, Rusu R B, et al. Real-time compression of point cloud streams[C]//Proceedings of the IEEE International Conference on Robotics and Automation. Los Alamitos: IEEE Computer Society Press, 2012: 778-785

[34]

Saranya M R, Senthil S T. Real-time compression strategy on various point cloud streams[J]. International Journal of Computer Science and Mobile Computing, 2014, 3(3): 351-358

[35]

Garcia D C, Fonseca T A, Ferreira R U, et al. Geometry coding for dynamic voxelized point clouds using octrees and multiple contexts[J]. IEEE Transactions on Image Processing, 2020, 29: 313-322

[36]

Liu J Q, Yao J, Tu J M, et al. Data-adaptive packing method for compression of dynamic point cloud sequences[C]//Proceedings of the IEEE International Conference on Multimedia and Expo. Los Alamitos: IEEE Computer Society Press, 2019: 904-909

[37]

Xu Y Z, Zhu W J, Xu Y L, et al. Dynamic point cloud geometry compression via patch-wise polynomial fitting[C]//Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Los Alamitos: IEEE Computer Society Press, 2019: 2287-2291

[38]

Ahmmed A, Paul M, Murshed M, et al. Dynamic point cloud compression using a cuboid oriented discrete cosine based motion model[C]//Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Los Alamitos: IEEE Computer Society Press, 2021: 1935-1939

[39]

Schwarz S, Preda M, Baroncini V, et al. Emerging MPEG standards for point cloud compression[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2019, 9(1): 133-148

[40]

Li L, Li Z, Zakharchenko V, et al. Advanced 3D motion prediction for video-based dynamic point cloud compression[J]. IEEE Transactions on Image Processing, 2020, 29: 289-302

[41]

Zhu W J, Ma Z, Xu Y L, et al. View-dependent dynamic point cloud compression[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(2): 765-781

[42]

Wang J Q, Ding D D, Li Z, et al. Multiscale point cloud geometry compression[C]//Proceedings of the Data Compression Conference. Los Alamitos: IEEE Computer Society Press, 2021: 73-82

[43]

Liu G X, Zhu J H, Ding D D, et al. Encoding auxiliary information to restore compressed point cloud geometry[C]//Proceedings of the 33rd International Joint Conference on Artificial Intelligence. Freiburg: IJCAI, 2024: 2189-2197

[44]

Wang J Q, Ding D D, Li Z, et al. Sparse tensor-based multiscale representation for point cloud geometry compression[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(7): 9055-9071

[45]

Quach M, Valenzise G, Dufaux F. Improved deep point cloud geometry compression[C]//Proceedings of the 22nd IEEE International Workshop on Multimedia Signal Processing. Los Alamitos: IEEE Computer Society Press, 2020: 1-6

[46]

Liu G X, Wang J Q, Ding D D, et al. PCGFormer: lossy point cloud geometry compression via local self-attention[C]//Proceedings of the IEEE International Conference on Visual Communications and Image Processing. Los Alamitos: IEEE Computer Society Press, 2022: 1-5

[47]

Wang J Q, Zhu H, Liu H J, et al. Lossy point cloud geometry compression via end-to-end learning[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(12): 4909-4923

[48]

Que Z Z, Lu G, Xu D. VoxelContext-Net: an octree based framework for point cloud compression[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2021: 6038-6047

[49]

Zhou X Y, Qi C R, Zhou Y, et al. Riddle: lidar data compression with range image deep delta encoding[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2022: 17191-17200

[50]

Liu C S, Yeh J F, Hsu H, et al. BIRD-PCC: bi-directional range image-based deep lidar point cloud compression[C]//Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Los Alamitos: IEEE Computer Society Press, 2023: 1-5

[51]

Gao L Y, Fan T Y, Wan J Q, et al. Point cloud geometry compression via neural graph sampling[C]//Proceedings of the IEEE International Conference on Image Processing. Los Alamitos: IEEE Computer Society Press, 2021: 3373-3377

[52]

Akhtar A, Li Z, van der Auwera G. Inter-frame compression for dynamic point cloud geometry coding[J]. IEEE Transactions on Image Processing, 2024, 33: 584-594

[53]

Fan T Y, Gao L Y, Xu Y L, et al. D-DPCC: deep dynamic point cloud compression via 3D motion prediction[C]//Proceedings of the 31st International Joint Conference on Artificial Intelligence. Freiburg: IJCAI, 2022: 898-904

[54]

Xia S T, Fan T Y, Xu Y L, et al. Learning dynamic point cloud compression via hierarchical inter-frame block matching[C]//Proceedings of the 31st ACM International Conference on Multimedia. New York: ACM Press, 2023: 7993-8003

[55]

Jiang Z Y, Wang G L, Tam G K L, et al. An end-to-end dynamic point cloud geometry compression in latent space[J]. Displays, 2023, 80: Article No.102528

[56]

Sun X B, Wang S K, Wang M H, et al. A novel coding architecture for LiDAR point cloud sequence[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 5637-5644

[57]

Beemelmanns T, Tao Y C, Lampe B, et al. 3D point cloud compression with recurrent neural network and image compression methods[C]//Proceedings of the IEEE Intelligent Vehicles Symposium. Los Alamitos: IEEE Computer Society Press, 2022: 345-351

[58]

de Queiroz R L, Chou P A. Compression of 3D point clouds using a region-adaptive hierarchical transform[J]. IEEE Transactions on Image Processing, 2016, 25(8): 3947-3956

[59]

Cohen R A, Tian D, Vetro A. Attribute compression for sparse point clouds using graph transforms[C]//Proceedings of the IEEE International Conference on Image Processing. Los Alamitos: IEEE Computer Society Press, 2016: 1374-1378

[60]

Mekuria R, Blom K, Cesar P. Design, implementation, and evaluation of a point cloud codec for tele-immersive video[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(4): 828-842

[61]

Thanou D, Chou P A, Frossard P. Graph-based compression of dynamic 3D point cloud sequences[J]. IEEE Transactions on Image Processing, 2016, 25(4): 1765-1778

[62]

Thanou D, Chou P A, Frossard P. Graph-based motion estimation and compensation for dynamic 3D point cloud compression[C]//Proceedings of the IEEE International Conference on Image Processing. Los Alamitos: IEEE Computer Society Press, 2015: 3235-3239

[63]

Hong H R, Pavez E, Ortega A, et al. Fractional motion estimation for point cloud compression[C]//Proceedings of the Data Compression Conference. Los Alamitos: IEEE Computer Society Press, 2022: 369-378

[64]

Xu Y Q, Hu W, Wang S S, et al. Predictive generalized graph Fourier transform for attribute compression of dynamic point clouds[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(5): 1968-1982

[65]

Souto A L, de Queiroz R L. On predictive RAHT for dynamic point cloud coding[C]//Proceedings of the IEEE International Conference on Image Processing. Los Alamitos: IEEE Computer Society Press, 2020: 2701-2705

[66]

Souto A L, De Queiroz R L, Dorea C. Motion-compensated predictive RAHT for dynamic point clouds[J]. IEEE Transactions on Image Processing, 2023, 32: 2428-2437

[67]

He Y, Ren X L, Tang D H, et al. Density-preserving deep point cloud compression[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2022: 2323-2332

[68]

Pan Z R, Xiao M B, Han X, et al. patchDPCC: a patchwise deep compression framework for dynamic point clouds[C]//Proceedings of the 38th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2024: 4406-4414

[69]

Wang J Q, Ding D D, Ma Z. Lossless point cloud attribute compression using cross-scale, cross-group, and cross-color prediction[C]//Proceedings of the Data Compression Conference. Los Alamitos: IEEE Computer Society Press, 2023: 228-237

[70]

Liu W, Gao W, Mu X M. Fast inter-frame motion prediction for compressed dynamic point cloud attribute enhancement[C]//Proceedings of the 38th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2024: 3720-3728

[71]

Anis A, Chou P A, Ortega A. Compression of dynamic 3D point clouds using subdivisional meshes and graph wavelet transforms[C]//Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Los Alamitos: IEEE Computer Society Press, 2016: 6360-6364

[72]

de Queiroz R L, Chou P A. Motion-compensated compression of dynamic voxelized point clouds[J]. IEEE Transactions on Image Processing, 2017, 26(8): 3886-3895

[73]

Biswas S, Liu J, Wong K, et al. MuSCLE: multi sweep compression of LiDAR using deep entropy models[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc, 2020: Article No.1859

[74]

Zhang J Z, Chen T, Ding D D, et al. G-PCC++: enhanced geometry-based point cloud compression[C]//Proceedings of the 31st ACM International Conference on Multimedia. New York: ACM Press, 2023: 1352-1363

[75]

Zhang J T, Chen T, Ding D D, et al. YOGA: yet another geometry-based point cloud compressor[C]//Proceedings of the 31st ACM International Conference on Multimedia. New York: ACM Press, 2023: 9070-9081

[76]

Zhang J Z, Liu G X, Zhang J T, et al. DeepPCC: learned lossy point cloud compression[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2025, 9(2): 1897-1909

[77]

Wang J Q, Xue R X, Li J X, et al. A versatile point cloud compressor using universal multiscale conditional coding–Part Ⅰ: geometry[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(1): 269-287

[78]

Wang J Q, Xue R X, Li J X, et al. A versatile point cloud compressor using universal multiscale conditional coding–Part Ⅱ: attribute[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(1): 252-268

[79]

d'Eon E, Harrison B, Myers T, et al. 8i voxelized full bodies-a voxelized point cloud dataset[R]. Geneva: MPEG, 2017

[80]

Xu Y, Lu Y, Wen Z Y. Owlii dynamic human mesh sequence dataset[OL]. [2024-11-16]. https://mpeg-pcc.org/index.php/pcc-content-database/owlii-dynamic-human-textured-mesh-sequence-datase

[81]

Loop C, Cai Q, Escolano S O, et al. Microsoft voxelized upper bodies-a voxelized point cloud dataset[R]. Geneva: MPEG, 2016

[82]

Behley J, Garbade M, Milioto A, et al. SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, 2019: 9296-9306

[83]

Liu T, Zhao Y, Wei Y C, et al. Concealed object detection for activate millimeter wave image[J]. IEEE Transactions on Industrial Electronics, 2019, 66(12): 9909-9917

[84]

Yang B S, Huang R G, Dong Z, et al. Two-step adaptive extraction method for ground points and breaklines from lidar point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 119: 373-389

[85]

Liu X Y, Qi C R, Guibas L J. FlowNet3D: learning scene flow in 3D point clouds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2019: 529-537

[86]

Pandey G, McBride J R, Eustice R M. Ford campus vision and lidar data set[J]. The International Journal of Robotics Research, 2011, 30(13): 1543-1552

[87]

Chang A X, Funkhouser T, Guibas L, et al. ShapeNet: an information-rich 3D model repository[OL]. [2024-11-16]. https://arxiv.org/abs/1512.03012

[88]

Bjøntegaard G. Calculation of average PSNR differences between RD-curves[C]//Proceedings of the ITU-T Video Coding Experts Group Thirteenth Meeting. Austin: ITU-T SG16, 2001: 1-15

[89]

Wang Changshuo, Wang Han, Ning Xin, et al. 3D point cloud classification method based on dynamic coverage of local area[J]. Journal of Software, 2022, 34(4): 1962-1976 (in Chinese) (王昌硕, 王含, 宁欣, 等. 基于局部区域动态覆盖的3D点云分类方法[J]. 软件学报, 2022, 34(4): 1962-1976)

[90]

Wang C S, Cao R S, Wang R P. Learning discriminative topological structure information representation for 2D shape and social network classification via persistent homology[J]. Knowledge-Based Systems, 2025, 311: Article No.113125

2025年第37卷第12期

PDF下载

108

引用本文

BibTeX

文章信息

doi: 10.3724/SP.J.1089.2024-00699

接收时间：2024-11-16
首发时间：2026-10-10
出版时间：2025-12-20

补充材料

相关文章

文章信息

作者

出版历史

收稿日期：2024-11-16
修回日期：2025-06-24

基金

国家自然科学基金(62172366)

浙江省“尖兵”“领雁”研发攻关计划(2023C01150)

作者信息

¹⁾浙江工商大学计算机科学与技术学院　杭州　310018

²⁾杭州师范大学信息科学与技术学院　杭州　311121

³⁾浙江工商大学统计与数学学院　杭州　310018

⁴⁾中国移动（杭州）信息技术有限公司　杭州　310023

⁵⁾杭州宇泛智能科技股份有限公司　杭州　311121

通讯作者:

（ybl@zjgsu.edu.cn）

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/jsjfzsj/CN/10.3724/SP.J.1089.2024-00699

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

关闭全屏

BibTeX
EndNote
RefWorks
TxT