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ZHANG Huiran, DONG Zhen, YANG Bisheng, HUANG Ronggang, XU Dazhan. Progress and Perspectives of Point Cloud Compression[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210103
Citation: ZHANG Huiran, DONG Zhen, YANG Bisheng, HUANG Ronggang, XU Dazhan. Progress and Perspectives of Point Cloud Compression[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210103

Progress and Perspectives of Point Cloud Compression

doi: 10.13203/j.whugis20210103
Funds:

The Fund of Beijing Key Laboratory of Urban Spatial Information Engineering (2020208)

  • Received Date: 2020-12-26
    Available Online: 2022-03-12
  • With the rapid development of the reality acquisition technologies, such as laser scanning and structured light scanning, point cloud has become a high-precision three-dimensional holographic representation for the physics world. As the third important data source, point cloud is very suitable for presenting 3D model and geographic and spatial information, and pushes forward an immense influence on smart city, autonomous driving application and augmented reality. However, the massive, unstructured, and uneven density of point cloud data brings challenges to onboard and offboard storage as well as real-time transmission. Hence, efficient compression methods, which balance between bit rate and quality, are mandatory for ensuring the storage and transmission of such data. This paper summarizes the state-of-the art of domestic and foreign static point cloud compression algorithms, the standard specifications released by Moving Picture Experts Group (MPEG) and evaluation metrics for point cloud compression. First, we describe different families of approaches in details and summarize the basic technologies that are usually used in 3D point cloud compression. Moreover, we provide detailed description of three open source point cloud codec algorithms and their coding performances. Finally, the promising development tendency of the static point cloud compression is summarized.
  • [1] Thanou D, Chou P A, Frossard P. Graph-based motion estimation and compensation for dynamic 3D point cloud compression[C]//IEEE International Conference on Image Processing. IEEE, 2015.
    [2] Thanou D, Chou P, Frossard P. Graph-based compression of dynamic 3D point cloud sequences[J]. IEEE Transactions on Image Processing, 2016:1765-1778.
    [3] Bletterer A, Payan F, Antonini M, et al. Point Cloud Compression using Depth Maps[J]. Electronic Imaging, 2016, 2016(21):1-6.
    [4] Anis A, Chou P A, Ortega A. Compression of dynamic 3D point clouds using subdivisional meshes and graph wavelet transforms[C]//IEEE International Conference on Acoustics. IEEE, 2016.
    [5] Yang R, Yan N, Li L, et al. Chain Code-Based Occupancy Map Coding for Video-Based Point Cloud Compression[C]. Visual Communications and Image Processing (VCIP), Macau, 2020, 479-482.
    [6] Queiroz R L D, Chou P A. Motion-Compensated Compression of Dynamic Voxelized Point Clouds[J]. IEEE Transactions on Image Processing, 2017, PP (99):1-1.
    [7] Li L, Li Z, Liu S, et al. Efficient Projected Frame Padding for Video-based Point Cloud Compression[J]. IEEE Transactions on Multimedia, 2020, PP (99):1-1.
    [8] MPEG 3DG, Call for proposals for point cloud compression v2, ISO/IEC JTC 1/SC 29/WG 11 N16763, 2017.
    [9] MPEG, "PCC Core Experiments 0.1 on Convergence between TMC1 and TMC3" ISO/IEC JTC1/SC29/WG11 MPEG, N 17352, Jan. 2018.
    [10] Cao C, Preda M, Zaharia T. 3D Point Cloud Compression:A Survey[C]//The 24th International Conference on 3D Web Technology. 2019.
    [11] Graziosi D, Nakagami O, Kuma S, et al. An overview of ongoing point cloud compression standardization activities:video-based (V-PCC) and geometry-based (G-PCC)[J]. APSIPA Transactions on Signal and Information Processing, 2020, 9.
    [12] Huang Y, Peng J, Kuo C C J, et al. Octree-Based Progressive Geometry Coding of Point Clouds[C]. Symposium on Point Based Graphics, Boston, Massachusetts, USA, 2006. Proceedings. Eurographics Association, 2006.
    [13] Hubo E, Mertens T, Haber T, Bekaert P. The Quantized kd-Tree:Efficient Ray Tracing of Compressed Point Clouds[C]. IEEE Symposium on Interactive Ray Tracing, Salt Lake City, UT, 2006, pp. 105-113.
    [14] Botsch M, R. Pajarola (editors, Schnabel R, et al. A Parallelly Decodeable Compression Scheme for Efficient Point-Cloud Rendering[C]. Symposium on Point Based Graphics. DBLP, 2007.
    [15] Sim J Y, Lee S U. Compression of 3-D Point Visual Data Using Vector Quantization and Rate-Distortion Optimization[J]. IEEE Transactions on Multimedia, 2008, 10(3):305-315.
    [16] Schnabel R, Möser S, Klein R. Fast vector quantization for efficient rendering of compressed point-clouds[J]. Computers&Graphics, 2008, 32(2):246-259.
    [17] Park S B, Lee S U. Multiscale Representation and Compression of 3-D Point Data[J]. IEEE Transactions on Multimedia, 2009, 11(1):177-183.
    [18] Ochotta T, Saupe D. Image-Based Surface Compression[J]. Computer Graphics Forum, 2010, 27(6):1647-1663.
    [19] Daribo I, Furukawa R, Sagawa R, et al. Point cloud compression for grid-pattern-based 3D scanning system[C]. Visual Communications and Image Processing (VCIP), Tainan, 2011, 1-4.
    [20] 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 (ICRA). IEEE, 2012.
    [21] Smith J, Petrova G, Schaefer S. Progressive encoding and compression of surfaces generated from point cloud data[J]. Computers&Graphics, 2012, 36(5):341-348.
    [22] Jiang W, Tian J, Cai K, et al. Tangent-plane-continuity maximization based 3D point compression[C]. 19th IEEE International Conference on Image Processing, Orlando, FL, 2012, 1277-1280.
    [23] Daribo I, Furukawa R, Sagawa R, et al. Adaptive arithmetic coding for point cloud compression[C]. 3DTV-Conference:The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON). IEEE, 2012.
    [24] Elseberg J, Borrmann D, Nuechter A. One billion points in the cloud:an octree for efficient processing of 3D laser scans[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 76(FEB.):76-88.
    [25] Isenburg, Martin. LASzip:lossless compression of LiDAR data[J]. Photogrammetric Engineering&Remote Sensing, 2013, 79(2):209-217.
    [26] Fan Y, Huang Y, Peng J. Point cloud compression based on hierarchical point clustering[C]. 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. IEEE, 2013.
    [27] Julie, Digne, Raphalle, et al. Self-similarity for accurate compression of point sampled surfaces[J]. Computer Graphics Forum, 2014, 33(2):155-164.
    [28] Zhang C, Florêncio D, Loop C. Point cloud attribute compression with graph transform[C]. IEEE Int. Conf. Image Process. Paris, France, 2014, Oct.
    [29] Morell V, Orts S, Cazorla M, et al. Geometric 3D point cloud compression[J]. Pattern Recognition Letters, 2014, 50:55-62.
    [30] Ahn J K, Lee K Y, Sim J Y, et al. Large-Scale 3D Point Cloud Compression Using Adaptive Radial Distance Prediction in Hybrid Coordinate Domains[J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(3):422-434.
    [31] Golla T, Klein R. Real-time point cloud compression[C]. IEEE/RSJ International Conference on Intelligent Robots&Systems. IEEE, 2015:5087-5092.
    [32] Houshiar H, Nuchter A. 3D point cloud compression using conventional image compression for efficient data transmission[C]. In Proceedings of the 2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT)(ICAT'15). IEEE Computer Society, USA, 1-8.
    [33] Bletterer, Arnaud, Payan, et al. Point Cloud Compression using Depth Maps[J]. Electronic Imaging, 2016.
    [34] Cohen R A, Tian D, Vetro A. Point Cloud Attribute Compression Using 3-D Intra Prediction and Shape-Adaptive Transforms[C]. Data Compression Conference. IEEE Computer Society, 2016:141-150.
    [35] Dado B, Kol T R, Bauszat P, et al. Geometry and Attribute Compression for Voxel Scenes[J]. Computer Graphics Forum, 2016, 35(2):397-407.
    [36] De Queiroz R, 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.
    [37] Mekuria R, Blom K, Cesar P. Design, Implementation and Evaluation of a Point Cloud Codec for Tele-Immersive Video[J]. IEEE Transactions on Circuits&Systems for Video Technology, 2016:1-1.
    [38] Cohen R A, Tian D, Vetro A. Attribute compression for sparse point clouds using graph transforms[C]. IEEE International Conference on Image Processing. IEEE, 2016.
    [39] Zhang X, Wan W, An X. Clustering and DCT Based Color Point Cloud Compression[J]. Journal of Signal Processing Systems, 2017.
    [40] Hou J, Chau L P, He Y, et al. Sparse representation for colors of 3D point cloud via virtual adaptive sampling[C]. IEEE International Conference on Acoustics. IEEE, 2017.
    [41] De Queiroz R, Chou P A. Transform Coding for Point Clouds Using a Gaussian Process Model[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2017:3507-3517.
    [42] Tu C, Takeuchi E, Miyajima C, et al. Continuous point cloud data compression using SLAM based prediction[C]. Intelligent Vehicles Symposium. IEEE, 2017.
    [43] Wang L, Wang L, Luo Y, et al. Point-cloud Compression Using Data Independent Method-A 3D Discrete Cosine Transform Approach[C]. IEEE International Conference on Information&Automation. IEEE, 2017.
    [44] Cui L, Xu H Y, Jang E S. Hybrid color attribute compression for point cloud data[C]. IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2017.
    [45] Cohen R A, Krivokuca M, Feng C, et al. Compression of 3-D point clouds using hierarchical patch fitting[C]. IEEE International Conference on Image Processing (ICIP). IEEE, 2017.
    [46] Milani S. Fast point cloud compression via reversible cellular automata block transform[J]. IEEE International Conference on Image Processing (ICIP). IEEE, 2017, 4013-4017.
    [47] Zhu W, Xu Y, Li L, et al. Lossless point cloud geometry compression via binary tree partition and intra prediction[C]. IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2017.
    [48] He C, Ran L, Wang L, et al. Point set surface compression based on shape pattern analysis[J]. Multimedia Tools and Applications, 2017, 76(20):20545-20565.
    [49] Gu S, Hou J, Zeng H, et al. Compression of 3D point clouds using 1D discrete cosine transform[C]. International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). 2017.
    [50] Shao Y, Zhang Z, Li L, et al. Attribute compression of 3D point clouds using Laplacian sparsity optimized graph transform[C]. IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, 2017, 1-4.
    [51] Xu Y, Wang S, Zhang X, et al. Rate-distortion optimized scan for point cloud color compression[C]. Visual Communications&Image Processing. IEEE, 2018.
    [52] Xu Y, Wei H, Wang S, et al. CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM[C]. Icassp IEEE International Conference on Acoustics. IEEE, 2018.
    [53] Zhang K, Zhu W, Xu Y, et al. Point Cloud Attribute Compression via Clustering and Intra Prediction[C]. 2018:1-5.
    [54] Kathariya B, Li L, Li Z, et al. Scalable Point Cloud Geometry Coding with Binary Tree Embedded Quadtree[C]. IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2018.
    [55] Dricot A, Pereira F, Ascenso J. Rate-Distortion Driven Adaptive Partitioning for Octree-Based Point Cloud Geometry Coding[C]. 2018:2969-2973.
    [56] Sandri G, De Queiroz R, Chou P A. Compression of Plenoptic Point Clouds Using the Region-Adaptive Hierarchical Transform[J]. 2018:1153-1157.
    [57] Garcia D C, Queiroz R L D. Intra-Frame Context-Based Octree Coding for Point-Cloud Geometry[C]. 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018.
    [58] Cao K, Xu Y, Cosman P C. PATCH-AWARE AVERAGING FILTER FOR SCALING IN POINT CLOUD COMPRESSION[C]. IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2018.
    [59] Sheikhi-Pour N, Schwarz S, Vadakital V K M, et al. Efficient 2D Video Coding of Volumetric Video Data[C]. 7th European Workshop on Visual Information Processing (EUVIP). 2018.
    [60] Sevom V F, Schwarz S, Gabbouj M. Geometry-Guided 3D Data Interpolation for Projection-Based Dynamic Point Cloud Coding[C]. 7th European Workshop on Visual Information Processing (EUVIP). 2018.
    [61] Filali A, Ricordel V, Normand N, et al. Rate-Distortion Optimized Tree-Structured Point-Lattice Vector Quantization for Compression of 3D Point Cloud Geometry[C]. International Conference on Image Processing (ICIP). 2019.
    [62] Krivokuća M, Koroteev M V, Chou P. A Volumetric Approach to Point Cloud Compression[J]. 2018.
    [63] Shao Y, Zhang Q, Li G, et al. Hybrid Point Cloud Attribute Compression Using Slice-based Layered Structure and Block-based Intra Prediction[C]. ACM Multimedia Conference. ACM, 2018.
    [64] Emerging MPEG Standards for Point Cloud Compression[J]. Emerging and Selected Topics in Circuits and Systems, IEEE Journal on, 2018.
    [65] Rente, Oliveira P D, Brites, et al. Graph-Based Static 3D Point Clouds Geometry Coding[J]. IEEE Transactions on Multimedia, 2019.
    [66] Imdad U, Asif M, Ahmad M, et al. Three Dimensional Point Cloud Compression and Decompression Using Polynomials of Degree One[J]. Symmetry, 2019, 11(2).
    [67] Sun X, Ma H, Sun Y, et al. A Novel Point Cloud Compression Algorithm Based on Clustering[J]. IEEE Robotics and Automation Letters, 2019, 4(2):2132-2139.
    [68] Gustavo, Sandri, Ricardo, et al. Compression of Plenoptic Point Clouds[J]. IEEE Transactions on Image Processing, 2019, 28(3):1419-1427.
    [69] Li L, Li Z, Zakharchenko V, et al. Advanced 3D Motion Prediction for Video Based Point Cloud Attributes Compression[C]. Data Compression Conference (DCC). 2019.
    [70] Kathariya B, Zakharchenko V, Li Z, et al. Level-of-Detail Generation Using Binary-Tree for Lifting Scheme in LiDAR Point Cloud Attributes Coding[C]. Data Compression Conference (DCC). 2019.
    [71] Quach M, Valenzise G, Dufaux F. Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression. 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 4320-4324.
    [72] Dricot A, Ascenso J. Hybrid Octree-Plane Point Cloud Geometry Coding[C]. 201927th European Signal Processing Conference (EUSIPCO). IEEE, 2019.
    [73] Dricot A, Ascenso J. Adaptive Multi-level Triangle Soup for Geometry-based Point Cloud Coding[C]。IEEE 21st International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2019.
    [74] Gu S, Hou J, Zeng H, et al. 3D Point Cloud Attribute Compression Using Geometry-Guided Sparse Representation[J]. IEEE Transactions on Image Processing, 2019, PP (99):1-1.
    [75] Fujihashi T, Koike-Akino T, Watanabe T, et al. HoloCast:Graph Signal Processing for Graceful Point Cloud Delivery[C]. International Conference on Communications (ICC). IEEE, 2019.
    [76] Yan W, Shao Y, Liu S, et al. Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds[J]. 2019.
    [77] Wang J, Zhu H, Ma Z, et al. Learned Point Cloud Geometry Compression[J]. 2019.
    [78] Chou P A, Koroteev M, Krivokuca M. A Volumetric Approach to Point Cloud Compression, Part I:Attribute Compression[J]. IEEE Transactions on Image Processing, 2019:1-1.
    [79] Krivokuca M, Chou P A, Koroteev M. A Volumetric Approach to Point Cloud Compression, Part II:Geometry Compression[J]. IEEE Transactions on Image Processing, 2019, 29.
    [80] Huang T, Liu Y. 3D Point Cloud Geometry Compression on Deep Learning[C]. In Proceedings of the 27th ACM International Conference on Multimedia (MM'19). Association for Computing Machinery, New York, NY, USA, 890-898.
    [81] Gu S, Hou J, Zeng H, et al. 3D Point Cloud Attribute Compression via Graph Prediction[J]. IEEE Signal Processing Letters, 2020, 27:176-180.
    [82] André F. R. Guarda, Rodrigues N M M, Pereira F. Point Cloud Coding:Adopting a Deep Learning-based Approach[C]. 2019 Picture Coding Symposium (PCS). IEEE, 2020.
    [83] Wei L, Wan S, Sun Z, et al. Weighted Attribute Prediction Based on Morton Code for Point Cloud Compression[C]. 2020 IEEE International Conference on Multimedia&Expo Workshops (ICMEW). IEEE, 2020.
    [84] Park J, Lee J, Park S, et al. Projection-based Occupancy Map Coding for 3D Point Cloud Compression[J]. IEIE Transactions on Smart Processing and Computing, 2020, 9(4):293-297.
    [85] Zhang X, Gao W, Liu S. Implicit Geometry Partition for Point Cloud Compression[C]. Data Compression Conference (DCC). 2020.
    [86] Pavez E, Girault B, Ortega A, et al. Region adaptive graph fourier transform for 3d point clouds[J]. 2020.
    [87] Feng Y, Liu S, Zhu Y. Real-Time Spatio-Temporal LiDAR Point Cloud Compression[J]. 2020.
    [88] Quach M, Valenzise G, Dufaux F. Folding-based compression of point cloud attributes[C]. 2020.
    [89] Huang L, Wang S, Wong K, et al. OctSqueeze:Octree-Structured Entropy Model for LiDAR Compression. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 1310-1320.
    [90] Maurice Q, Giuseppe V, Frederic D. Improved Deep Point Cloud Geometry Compression[J]. 2020.
    [91] Wen X, Wang X, Hou J, et al. Lossy Geometry Compression Of 3d Point Cloud Data Via An Adaptive Octree-Guided Network[C]. IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2020.
    [92] Nguyen D T, Quach M, Valenzise G, et al. Learning-based lossless compression of 3D point cloud geometry[J]. 2020.
    [93] Wang J, Ding D, Li Zhu et al. Multiscale Point Cloud Geometry Compression[J]. 2020.
    [94] Milani S. A Syndrome-Based Autoencoder For Point Cloud Geometry Compression[C].IEEE International Conference on Image Processing (ICIP). IEEE, 2020.
    [95] Ma C, Li G, Zhang Q, et al. Fast Recolor Prediction Scheme in Point Cloud Attribute Compression. IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2020.
    [96] 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, 2018.
    [97] C. Loop, Q. Cai, S. O. Escolano, and P. A. Chou, "Microsoft voxelized upper bodies-Avoxelized point cloud dataset," ISO/IEC JTC1/SC29 Joint WG11/WG1(MPEG/JPEG) input document m38673/M72012, 2016.
    [98] E. d'Eon, B. Harrison, T. Myers, and P. A. Chou, "8i voxelized full bodies-A voxelized point cloud dataset," ISO/IEC JTC1/SC29 JointWG11/WG1(MPEG/JPEG) input document WG11M40059/WG1M74006, 2017.
    [99] MPEG Requirements Subgroup. Evaluation criteria for point cloud compression. Doc. ISO/IEC JTC1/SC29/WG11/N16332, Geneva, Switzerland, Jun. 2016.
    [100] Tian D, Ochimizu H, Feng C, et al. Geometric distortion metrics for point cloud compression[C]//2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017.
    [101] Huynh-Thu Q, Ghanbari M. Scope of validity of PSNR in image/video quality assessment[J]. Electronics letters, 2008, 44(13):800-801.
    [102] Sheikh H R, Bovik A C, De Veciana G. An information fidelity criterion for image quality assessment using natural scene statistics[J]. IEEE Transactions on image processing, 2005, 14(12):2117-2128.
    [103] Sheikh H R, Bovik A C. Image information and visual quality[J]. IEEE Transactions on image processing, 2006, 15(2):430-444.
    [104] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE transactions on image processing, 2004, 13(4):600-612.
    [105] Draco.Accessed:January 21, 2020.[Online].Available:https://github.com/google/draco.
    [106] Rossignac J. 3D compression made simple:Edgebreaker with ZipandWrap on a corner-table[J]. Proceedings International Conference on Shape Modeling and Applications, Genova, Italy, 2001, pp. 278-283.
    [107] Jarek D. Asymmetric numeral systems:entropy coding combining speed of Huffman coding with compression rate of arithmetic coding. 2013.
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Progress and Perspectives of Point Cloud Compression

doi: 10.13203/j.whugis20210103
Funds:

The Fund of Beijing Key Laboratory of Urban Spatial Information Engineering (2020208)

Abstract: With the rapid development of the reality acquisition technologies, such as laser scanning and structured light scanning, point cloud has become a high-precision three-dimensional holographic representation for the physics world. As the third important data source, point cloud is very suitable for presenting 3D model and geographic and spatial information, and pushes forward an immense influence on smart city, autonomous driving application and augmented reality. However, the massive, unstructured, and uneven density of point cloud data brings challenges to onboard and offboard storage as well as real-time transmission. Hence, efficient compression methods, which balance between bit rate and quality, are mandatory for ensuring the storage and transmission of such data. This paper summarizes the state-of-the art of domestic and foreign static point cloud compression algorithms, the standard specifications released by Moving Picture Experts Group (MPEG) and evaluation metrics for point cloud compression. First, we describe different families of approaches in details and summarize the basic technologies that are usually used in 3D point cloud compression. Moreover, we provide detailed description of three open source point cloud codec algorithms and their coding performances. Finally, the promising development tendency of the static point cloud compression is summarized.

ZHANG Huiran, DONG Zhen, YANG Bisheng, HUANG Ronggang, XU Dazhan. Progress and Perspectives of Point Cloud Compression[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210103
Citation: ZHANG Huiran, DONG Zhen, YANG Bisheng, HUANG Ronggang, XU Dazhan. Progress and Perspectives of Point Cloud Compression[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210103
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