Perceptual Quality Assessment for Point Clouds : A Survey

doi:10.12142/ZTECOM.202304002

ZTE Communications ›› 2023, Vol. 21 ›› Issue (4): 3-16.DOI: 10.12142/ZTECOM.202304002

• Special Topic • Previous Articles Next Articles

Perceptual Quality Assessment for Point Clouds : A Survey

ZHOU Yingjie(), ZHANG Zicheng(), SUN Wei, MIN Xiongkuo, ZHAI Guangtao

Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2023-10-07 Online:2023-12-07 Published:2023-12-07
About author:ZHOU Yingjie (zyj2000@sjtu.edu.cn) received his BE degree in electronics and information engineering from China University of Mining and Technology in 2023. He is currently pursuing a PhD degree at the Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, China. His current research interests include 3D quality assessment and virtual digital human.
ZHANG Zicheng (zzc1998@sjtu.edu.cn) received his BE degree from Shanghai Jiao Tong University, China in 2020 and he is currently pursuing a PhD degree at the Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University. His research interest include image quality assessment, video quality assessment, and 3D visual quality assessment.
SUN Wei received his BE degree from the East China University of Science and Technology, China in 2016. He is currently pursuing a PhD degree at the Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, China. His research interests include image quality assessment, perceptual signal processing, and mobile video processing.
MIN Xiongkuo received his BE degree from Wuhan University, China in 2013, and PhD degree from Shanghai Jiao Tong University, China in 2018. From January 2016 to January 2017, he was a visiting student with the University of Waterloo, Canada. From June 2018 to September 2021, he was a postdoctoral researcher with Shanghai Jiao Tong University. From January 2019 to January 2021, he was a visiting postdoctoral researcher with The University of Texas at Austin, USA and the University of Macau, China. He is currently a tenure-track associate professor with the Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University. His research interests include image/video/audio quality assessment, quality of experience, visual attention modeling, extended reality, and multimodal signal processing.
ZHAI Guangtao received his BE and ME degrees from Shandong University, China in 2001 and 2004, respectively, and PhD degree from Shanghai Jiao Tong University, China in 2009. From 2008 to 2009, he was a visiting student with the Department of Electrical and Computer Engineering, McMaster University, Canada, where he was a postdoctoral fellow from 2010 to 2012. From 2012 to 2013, he was a Humboldt Research Fellow with the Institute of Multimedia Communication and Signal Processing, Friedrich Alexander University of Erlangen–Nuremberg, Germany. He is currently a professor with the Department of Electronics Engineering, Shanghai Jiao Tong University. He has published more than 100 journal articles on the topics including visual information acquisition, image processing, and perceptual signal processing.

Abstract

Abstract:

A point cloud is considered a promising 3D representation that has achieved wide applications in several fields. However, quality degradation inevitably occurs during its acquisition and generation, communication and transmission, and rendering and display. Therefore, how to accurately perceive the visual quality of point clouds is a meaningful topic. In this survey, we first introduce the point cloud to emphasize the importance of point cloud quality assessment (PCQA). A review of subjective PCQA is followed, including common point cloud distortions, subjective experimental setups and subjective databases. Then we review and compare objective PCQA methods in terms of model-based and projection-based. Finally, we provide evaluation criteria for objective PCQA methods and compare the performances of various methods across multiple databases. This survey provides an overview of classical methods and recent advances in PCQA.

Key words: point cloud quality assessment, PCQA databases, subjective quality assessment, objective quality assessment

ZHOU Yingjie, ZHANG Zicheng, SUN Wei, MIN Xiongkuo, ZHAI Guangtao. Perceptual Quality Assessment for Point Clouds : A Survey[J]. ZTE Communications, 2023, 21(4): 3-16.

Figures/Tables 9

References 108

1	CAO C, PREDA M, ZAHARIA T. 3D point cloud compression: a survey [C]//24th International Conference on 3D Web Technology. ACM, 2019: 1–9. DOI: 10.1145/3329714.3338130
2	CHARLES R Q, HAO S, MO K C, et al. PointNet: deep learning on point sets for 3D classification and segmentation [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017: 77–85. DOI: 10.1109/CVPR.2017.16
3	GUO Y L, WANG H Y, HU Q Y, et al. Deep learning for 3D point clouds: a survey [J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 43(12): 4338–4364. DOI: 10.1109/TPAMI.2020.3005434
4	LIU Y P, YANG Q, XU Y L, et al. Point cloud quality assessment: dataset construction and learning-based no-reference metric [J]. ACM transactions on multimedia computing, communications, and applications, 2023, 19(2s): No.80. DOI: 10.1145/3550274
5	LIU Q, SU H L, DUANMU Z F, et al. Perceptual quality assessment of colored 3D point clouds [J]. IEEE transactions on visualization and computer graphics, 2023, 29(8): 3642–3655. DOI: 10.1109/TVCG.2022.3167151
6	YANG Q, CHEN H, MA Z, et al. Predicting the perceptual quality of point cloud: a 3D-to-2D projection-based exploration [J]. IEEE transactions on multimedia, 2021, 23: 3877–3891. DOI: 10.1109/TMM.2020.3033117
7	LAZZAROTTO D, TESTOLINA M, EBRAHIMI T. On the impact of spatial rendering on point cloud subjective visual quality assessment [C]//14th International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2022: 1–6. DOI: 10.1109/QoMEX55416.2022.9900898
8	ALEXIOU E, EBRAHIMI T. On the performance of metrics to predict quality in point cloud representations [C]//Proc. SPIE 10396, Applications of Digital Image Processing XL. SPIE, 2017: 282–297. DOI: 10.1117/12.2275142
9	NEHMÉ Y, FARRUGIA J P, DUPONT F, et al. Comparison of subjective methods, with and without explicit reference, for quality assessment of 3D graphics [C]//ACM Symposium on Applied Perception. ACM, 2019: 1–9. DOI: 10.1145/3343036.3352493
10	EBRAHIMI T, ALEXIOU E, FONSECA T A, et al. A novel methodology for quality assessment of voxelized point clouds [C]//Proc. Applications of Digital Image Processing XLI. SPIE, 2018. DOI: 10.1117/12.2322741
11	ALEXIOU E, VIOLA I, BORGES T M, et al. A comprehensive study of the rate-distortion performance in MPEG point cloud compression [J]. APSIPA transactions on signal and information processing, 2019, 8(1): e27. DOI: 10.1017/atsip.2019.20
12	JAVAHERI A, BRITES C, PEREIRA F, et al. Subjective and objective quality evaluation of 3D point cloud denoising algorithms [C]//2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2017: 1–6. DOI: 10.1109/ICMEW.2017.8026263
13	ALEXIOU E, EBRAHIMI T. Impact of visualisation strategy for subjective quality assessment of point clouds [C]//2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2018: 1–6. DOI: 10.1109/ICMEW.2018.8551498
14	ALEXIOU E, PINHEIRO A M G, DUARTE C, et al. Point cloud subjective evaluation methodology based on reconstructed surfaces [C]//Proc. SPIE 10752, Applications of Digital Image Processing XLI. SPIE, 2018, 10752: 160–173. DOI: 10.1117/12.2321518
15	JAVAHERI A, BRITES C, PEREIRA F, et al. Subjective and objective quality evaluation of compressed point clouds [C]//IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2017: 1–6. DOI: 10.1109/MMSP.2017.8122239
16	SILVA CRUZ L A DA, DUMIĆ E, ALEXIOU E, et al. Point cloud quality evaluation: towards a definition for test conditions [C]//2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2019: 1–6. DOI: 10.1109/QoMEX.2019.8743258
17	ZERMAN E, GAO P, OZCINAR C, et al. Subjective and objective quality assessment for volumetric video compression [J]. Electronic imaging, 2019, 31(10): 323–1. DOI: 10.2352/issn.2470-1173.2019.10.iqsp-323
18	SU H L, DUANMU Z F, LIU W T, et al. Perceptual quality assessment of 3d point clouds [C]//2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019: 3182–3186. DOI: 10.1109/ICIP.2019.8803298
19	JAVAHERI A, BRITES C, PEREIRA F, et al. Point cloud rendering after coding: impacts on subjective and objective quality [J]. IEEE transactions on multimedia, 2021, 23: 4049–4064. DOI: 10.1109/TMM.2020.3037481
20	ZERMAN E, OZCINAR C, GAO P, et al. Textured mesh vs coloured point cloud: a subjective study for volumetric video compression [C]//Twelfth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2020: 1–6. DOI: 10.1109/QoMEX48832.2020.9123137
21	CAO K M, XU Y, COSMAN P. Visual quality of compressed mesh and point cloud sequences [J]. IEEE access, 2020, 8: 171203–171217. DOI: 10.1109/ACCESS.2020.3024633
22	ALEXIOU E, EBRAHIMI T. On subjective and objective quality evaluation of point cloud geometry [C]//Ninth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2017: 1–3. DOI: 10.1109/QoMEX.2017.7965681
23	ALEXIOU E, EBRAHIMI T. Point cloud quality assessment metric based on angular similarity [C]//2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2018: 1–6. DOI: 10.1109/ICME.2018.8486512
24	ALEXIOU E, EBRAHIMI T. Benchmarking of objective quality metrics for colorless point clouds [C]//2018 Picture Coding Symposium (PCS). IEEE, 2018: 51–55. DOI: 10.1109/PCS.2018.8456252
25	ALEXIOU E, EBRAHIMI T. Exploiting user interactivity in quality assessment of point cloud imaging [C]//Eleventh International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2019: 1–6. DOI: 10.1109/QoMEX.2019.8743277
26	VIOLA I, SUBRAMANYAM S, CESAR P. A color-based objective quality metric for point cloud contents [C]//Twelfth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2020: 1–6. DOI: 10.1109/QoMEX48832.2020.9123089
27	JAVAHERI A, BRITES C, PEREIRA F, et al. Improving PSNR-based quality metrics performance for point cloud geometry [C]//2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020: 3438–3442. DOI: 10.1109/ICIP40778.2020.9191233
28	HUA L, YU M, JIANG G Y, et al. VQA-CPC: a novel visual quality assessment metric of color point clouds [C]//Proc. SPIE 11550, Optoelectronic Imaging and Multimedia Technology VII. SPIE, 2020, 11550: 244–252. DOI: 10.1117/12.2573686
29	ALEXIOU E, UPENIK E, EBRAHIMI T. Towards subjective quality assessment of point cloud imaging in augmented reality [C]//IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2017: 1–6. DOI: 10.1109/MMSP.2017.8122237
30	ALEXIOU E, EBRAHIMI T, BERNARDO M V, et al. Point cloud subjective evaluation methodology based on 2D rendering [C]//Tenth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2018: 1–6. DOI: 10.1109/QoMEX.2018.8463406
31	PERRY S, CONG H P, SILVA CRUZ L A DA, et al. Quality evaluation of static point clouds encoded using MPEG codecs [C]//2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020: 3428–3432. DOI: 10.1109/ICIP40778.2020.9191308
32	SOLIMINI A G. Are there side effects to watching 3D movies? A prospective crossover observational study on visually induced motion sickness [J]. PLoS one, 2013, 8(2): e56160. DOI: 10.1371/journal.pone.0056160
33	SHARPLES S, COBB S, MOODY A, et al. Virtual reality induced symptoms and effects (VRISE): comparison of head mounted display (HMD), desktop and projection display systems [J]. Displays, 2008, 29(2): 58–69. DOI: 10.1016/j.displa.2007.09.005
34	SUBRAMANYAM S, LI J, VIOLA I, et al. Comparing the quality of highly realistic digital humans in 3DoF and 6DoF: a volumetric video case study [C]//2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). IEEE, 2020: 127–136. DOI: 10.1109/VR46266.2020.00031
35	ALEXIOU E, YANG N Y, EBRAHIMI T. PointXR: a toolbox for visualization and subjective evaluation of point clouds in virtual reality [C]//Twelfth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2020: 1–6. DOI: 10.1109/QoMEX48832.2020.9123121
36	WU X J, ZHANG Y, FAN C L, et al. Subjective quality database and objective study of compressed point clouds with 6DoF head-mounted display [J]. IEEE transactions on circuits and systems for video technology, 2021, 31(12): 4630–4644. DOI: 10.1109/TCSVT.2021.3101484
37	ZHANG J, HUANG W B, ZHU X Q, et al. A subjective quality evaluation for 3D point cloud models [C]//2014 International Conference on Audio, Language and Image Processing. IEEE, 2015: 827–831. DOI: 10.1109/ICALIP.2014.7009910
38	GUTIÉRREZ J, VIGIER T, LE CALLET P. Quality evaluation of 3D objects in mixed reality for different lighting conditions [J]. Electronic imaging, 2020, 32(11): no. 128. DOI: 10.2352/issn.2470-1173.2020.11.hvei-128
39	TURK G, LEVOY M. Zippered polygon meshes from range images [C]//21st Annual Conference on Computer Graphics and Interactive Techniques. ACM, 1994: 311–318. DOI: 10.1145/192161.192241
40	MPEG-PCC. MPEG point cloud datasets [DB/OL]. (2017-01-15)[2018-05-26].
41	JPEG. JPEG pleno database [DB/OL]. (2016-11-04)[2018-04-12].
42	HUA L, YU M, HE Z Y, et al. CPC-GSCT: visual quality assessment for coloured point cloud based on geometric segmentation and colour transformation [J]. IET image processing, 2022, 16(4): 1083–1095. DOI: 10.1049/ipr2.12211
43	AK A, ZERMAN E, QUACH M, et al. BASICS: broad quality assessment of static point clouds in compression scenarios [EB/OL]. (2023-02-09)[2023-05-06].
44	LIU Q, YUAN H, HAMZAOUI R, et al. Reduced reference perceptual quality model with application to rate control for video-based point cloud compression [J]. IEEE transactions on image processing, 2021, 30: 6623–6636. DOI: 10.1109/TIP.2021.3096060
45	LIU Q, SU H L, CHEN T X, et al. No-reference bitstream-layer model for perceptual quality assessment of V-PCC encoded point clouds [J]. IEEE transactions on multimedia, 2023, 25: 4533–4546. DOI: 10.1109/TMM.2022.3177926
46	QUACH M, VALENZISE G, DUFAUX F. Improved deep point cloud geometry compression [C]//IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2020: 1–6. DOI: 10.1109/MMSP48831.2020.9287077
47	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, 2018: 3460–3464. DOI: 10.1109/ICIP.2017.8296925
48	MEKURIA R, BLOM K, Design CESAR P., 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. DOI: 10.1109/TCSVT.2016.2543039
49	MEKURIA R, LI Z, TULVAN C, et al. Evaluation criteria for PCC (point cloud compression): MPEG: MPEG-I 2016/n16332 [S]. 2016
50	MEYNET G, DIGNE J, LAVOUÉ G. et al: A quality metric for 3D point clouds [C]//Eleventh International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2019: 1–3. DOI: 10.1109/QoMEX.2019.8743313
51	JAVAHERI A, BRITES C, PEREIRA F, et al. A generalized Hausdorff distance based quality metric for point cloud geometry [C]//Twelfth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2020: 1–6. DOI: 10.1109/QoMEX48832.2020.9123087
52	ALEXIOU E, EBRAHIMI T. Towards a point cloud structural similarity metric [C]//2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2020: 1–6. DOI: 10.1109/ICMEW46912.2020.9106005
53	MEYNET G, NEHMÉ Y, DIGNE J, et al. PCQM: A full-reference quality metric for colored 3D point clouds [C]//Twelfth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2020: 1–6. DOI: 10.1109/QoMEX48832.2020.9123147
54	JAVAHERI A, BRITES C, PEREIRA F, et al. Mahalanobis based point to distribution metric for point cloud geometry quality evaluation [J]. IEEE signal processing letters, 2020, 27: 1350–1354. DOI: 10.1109/LSP.2020.3010128
55	YANG Q, MA Z, XU Y L, et al. Inferring point cloud quality via graph similarity [J]. IEEE transactions on pattern analysis and machine intelligence, 2022, 44(6): 3015–3029. DOI: 10.1109/TPAMI.2020.3047083
56	DINIZ R, FREITAS P G, FARIAS M C Q. Multi-distance point cloud quality assessment [C]//2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020: 3443–3447. DOI: 10.1109/ICIP40778.2020.9190956
57	DINIZ R, FREITAS P G, FARIAS M C Q. Towards a point cloud quality assessment model using local binary patterns [C]//Twelfth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2020: 1–6. DOI: 10.1109/QoMEX48832.2020.9123076
58	DINIZ R, FREITAS P G, FARIAS M. A novel point cloud quality assessment metric based on perceptual color distance patterns [C]//IS&T International Symposium on Electronic Imaging Science and Technology 2021, Image Quality and System Performance XVIII. IS&T, 2021. DOI: 10.2352/issn.2470-1173.2021.9.iqsp-256
59	DINIZ R, FREITAS P G, FARIAS M C Q. Local luminance patterns for point cloud quality assessment [C]//IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2020: 1–6. DOI: 10.1109/MMSP48831.2020.9287154
60	DINIZ R, FREITAS P G, FARIAS M C Q. Color and geometry texture descriptors for point-cloud quality assessment [J]. IEEE signal processing letters, 2021, 28: 1150–1154. DOI: 10.1109/LSP.2021.3088059
61	HUA L, JIANG G Y, YU M, et al. BQE-CVP: blind quality evaluator for colored point cloud based on visual perception [C]//2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, 2021: 1–6. DOI: 10.1109/BMSB53066.2021.9547070
62	XU Y L, YANG Q, YANG L, et al. EPES: point cloud quality modeling using elastic potential energy similarity [J]. IEEE transactions on broadcasting, 2022, 68(1): 33–42. DOI: 10.1109/TBC.2021.3114510
63	ZHANG Y J, YANG Q, XU Y L. MS-GraphSIM: inferring point cloud quality via multiscale graph similarity [C]//29th ACM International Conference on Multimedia. ACM, 2021: 1230–1238. DOI: 10.1145/3474085.3475294
64	ZHANG Z C, SUN W, MIN X K, et al. No-reference quality assessment for 3D colored point cloud and mesh models [J]. IEEE transactions on circuits and systems for video technology, 2022, 32(11): 7618–7631. DOI: 10.1109/TCSVT.2022.3186894
65	HE Z Y, JIANG G Y, JIANG Z D, et al. Towards a colored point cloud quality assessment method using colored texture and curvature projection [C]//2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021: 1444–1448. DOI: 10.1109/ICIP42928.2021.9506762
66	TAO W X, JIANG G Y, JIANG Z D, et al. Point cloud projection and multi-scale feature fusion network based blind quality assessment for colored point clouds [C]//29th ACM International Conference on Multimedia. ACM, 2021: 5266–5272. DOI: 10.1145/3474085.3475645
67	LIU Q, YUAN H, SU H L, et al. PQA-net: Deep no reference point cloud quality assessment via multi-view projection [J]. IEEE transactions on circuits and systems for video technology, 2021, 31(12): 4645–4660. DOI: 10.1109/TCSVT.2021.3100282
68	CIGNONI P, ROCCHINI C, SCOPIGNO R. Metro: measuring error on simplified surfaces [J]. Computer graphics forum, 1998, 17(2): 167–174. DOI: 10.1111/1467-8659.00236
69	TIAN D, OCHIMIZU H, FENG C, et al. Evaluation metrics for point cloud compression: ISO/IEC m74008 [S]. 2017,
70	HE Z Y, JIANG G Y, YU M, et al. TGP-PCQA: texture and geometry projection based quality assessment for colored point clouds [J]. Journal of visual communication and image representation, 2022, 83: 103449. DOI: 10.1016/j.jvcir.2022.103449
71	TU R W, JIANG G Y, YU M, et al. Pseudo-reference point cloud quality measurement based on joint 2-D and 3-D distortion description [J]. IEEE transactions on instrumentation and measurement, 2023, 72: No.5019314. DOI: 10.1109/TIM.2023.3290291
72	VIOLA I, CESAR P. A reduced reference metric for visual quality evaluation of point cloud contents [J]. IEEE signal processing letters, 2020, 27: 1660–1664. DOI: 10.1109/LSP.2020.3024065
73	LIU Y P, YANG Q, XU Y L. Reduced reference quality assessment for point cloud compression [C]//2022 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2023: 1–5. DOI: 10.1109/VCIP56404.2022.10008813
74	ZHOU W, YUE G H, ZHANG R Z, et al. Reduced-reference quality assessment of point clouds via content-oriented saliency projection [J]. IEEE signal processing letters, 2023, 30: 354–358. DOI: 10.1109/LSP.2023.3264105
75	SU H L, LIU Q, LIU Y X, et al. Bitstream-based perceptual quality assessment of compressed 3D point clouds [J]. IEEE transactions on image processing, 2023, 32: 1815–1828. DOI: 10.1109/TIP.2023.3253252
76	ZHOU W, YANG Q, JIANG Q P, et al. Blind quality assessment of 3D dense point clouds with structure guided resampling [EB/OL]. (2022-08-31)[2022-09-05].
77	LIU Q, LIU Y Y, SU H L, et al. Progressive knowledge transfer based on human visual perception mechanism for perceptual quality assessment of point clouds [EB/OL]. (2022-11-30)[2022-12-04].
78	TU R W, JIANG G Y, YU M, et al. V-PCC projection based blind point cloud quality assessment for compression distortion [J]. IEEE transactions on emerging topics in computational intelligence, 2023, 7(2): 462–473. DOI: 10.1109/TETCI.2022.3201619
79	SHAN Z Y, YANG Q, YE R, et al. GPA-net: no-reference point cloud quality assessment with multi-task graph convolutional network [J]. IEEE transactions on visualization and computer graphics, 2802, 99: 1–13. DOI: 10.1109/TVCG.2023.3282802
80	ZHANG Z C, SUN W, WU H N, et al. GMS-3DQA: projection-based grid mini-patch sampling for 3D model quality assessment [EB/OL]. (2023-06-09)[2023-07-03].
81	LIU Y, YANG Q, ZHANG Y, et al. Once-training-all-fine: no-reference point cloud quality assessment via domain-relevance degradation description [EB/OL]. (2023-07-04)[2023-07-12].
82	YANG Q, LIU Y P, CHEN S H, et al. No-reference point cloud quality assessment via domain adaptation [C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022: 21147–21156. DOI: 10.1109/CVPR52688.2022.02050
83	FAN Y, ZHANG Z C, SUN W, et al. A No-reference quality assessment metric for point cloud based on captured video sequences [C]//24th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2022: 1–5. DOI: 10.1109/MMSP55362.2022.9949359
84	ZHANG Z C, SUN W, ZHU Y C, et al. Treating point cloud as moving camera videos: a no-reference quality assessment metric [EB/OL]. (2022-09-11)[2022-10-12].
85	ZHANG Z C, SUN W, MIN X K, et al. MM-PCQA: multi-modal learning for no-reference point cloud quality assessment [EB/OL]. (2022-09-01)[2022-09-27].
86	LAVOUÉ G, GELASCAE D, DUPONTF, et al. Perceptually driven 3D distance metrics with application to watermarking [C]//Proc. SPIE 6312, Applications of Digital Image Processing XXIX, SPIE. 2006, 6312: 150–161. DOI: 10.1117/12.686964
87	LAVOUÉ G. A multiscale metric for 3D mesh visual quality assessment [J]. Computer graphics forum, 2011, 30(5): 1427–1437. DOI: 10.1111/j.1467-8659.2011.02017.x
88	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. DOI: 10.1109/TIP.2003.819861
89	WANG Z, BOVIK A C. Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures [J]. IEEE signal processing magazine, 2009, 26(1): 98–117. DOI: 10.1109/MSP.2008.930649
90	SUN W, MIN X K, TU D Y, et al. Blind quality assessment for in-the-wild images via hierarchical feature fusion and iterative mixed database training [J]. IEEE journal of selected topics in signal processing, 2023, PP(99): 1–15. DOI: 10.1109/JSTSP.2023.3270621
91	MITTAL A, MOORTHY A K, BOVIK A C. No-reference image quality assessment in the spatial domain [J]. IEEE transactions on image processing, 2012, 21(12): 4695–4708. DOI: 10.1109/TIP.2012.2214050
92	NARVEKAR N D, KARAM L J. A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection [C]//2009 International Workshop on Quality of Multimedia Experience. IEEE, 2009: 87–91. DOI: 10.1109/QOMEX.2009.5246972
93	ZHANG L, ZHANG L, BOVIK A C. A feature-enriched completely blind image quality evaluator [J]. IEEE transactions on image processing, 2015, 24(8): 2579–2591. DOI: 10.1109/TIP.2015.2426416
94	GU K, ZHAI G T, YANG X K, et al. Using free energy principle for blind image quality assessment [J]. IEEE transactions on multimedia, 2015, 17(1): 50–63. DOI: 10.1109/TMM.2014.2373812
95	GU K, ZHAI G T, YANG X K, et al. No-reference image quality assessment metric by combining free energy theory and structural degradation model [C]//2013 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2013: 1–6. DOI: 10.1109/ICME.2013.6607462
96	MITTAL A, SOUNDARARAJAN R, BOVIK A C. Making a “completely blind” image quality analyzer [J]. IEEE signal processing letters, 2013, 20(3): 209–212. DOI: 10.1109/LSP.2012.2227726
97	ZHANG W X, MA K D, YAN J, et al. Blind image quality assessment using a deep bilinear convolutional neural network [J]. IEEE transactions on circuits and systems for video technology, 2020, 30(1): 36–47. DOI: 10.1109/TCSVT.2018.2886771
98	HARA K, KATAOKA H, SATOH Y. Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and ImageNet? [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2018: 6546–6555. DOI: 10.1109/CVPR.2018.0068
99	VQEG. Final report from the video quality experts group on the validation of objective models of video quality assessment: PHASE II [R]. 2003
100	WANG Z, SIMONCELLI E P, BOVIK A C. Multiscale structural similarity for image quality assessment [C]//Thrity-Seventh Asilomar Conference on Signals, Systems & Computers. IEEE, 2004: 1398–1402. DOI: 10.1109/ACSSC.2003.1292216
101	SHEIKH H R, BOVIK A C. Image information and visual quality [J]. IEEE transactions on image processing, 2006, 15(2): 430–444. DOI: 10.1109/TIP.2005.859378
102	MITTAL A, SAAD M A, BOVIK A C. A completely blind video integrity oracle [J]. IEEE transactions on image processing. IEEE, 2016, 25(1): 289–300. DOI: 10.1109/TIP.2015.2502725
103	SAAD M A, BOVIK A C, CHARRIER C. Blind prediction of natural video quality [J]. IEEE transactions on image processing, 2014, 23(3): 1352–1365. DOI: 10.1109/TIP.2014.2299154
104	KORHONEN J. Two-level approach for no-reference consumer video quality assessment [J]. IEEE transactions on image processing, 2019, 28(12): 5923–5938. DOI: 10.1109/TIP.2019.2923051
105	TU Z Z, WANG Y L, BIRKBECK N, et al. UGC-VQA: benchmarking blind video quality assessment for user generated content [J]. IEEE transactions on image processing. IEEE, 2021, 30: 4449–4464. DOI: 10.1109/TIP.2021.3072221
106	LI D Q, JIANG T T, JIANG M. Quality assessment of in-the-wild videos [C]//27th ACM International Conference on Multimedia. New York: ACM, 2019: 2351–2359. DOI: 10.1145/3343031.3351028
107	TU Z Z, YU X X, WANG Y L, et al. RAPIQUE: rapid and accurate video quality prediction of user generated content [J]. IEEE open journal of signal processing, 2021, 2: 425–440. DOI: 10.1109/OJSP.2021.3090333
108	SUN W, WANG T, MIN X K, et al. Deep learning based full-reference and no-reference quality assessment models for compressed UGC videos [C]//2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2021: 1–6. DOI: 10.1109/ICMEW53276.2021.9455999

Related Work	Display	Interaction	Methodology
Work of ALEXIOU et al.^[14]	2D monitor	×	DSIS
Work of ALEXIOU and EBRAHIMI^[13]	2D monitor	×	DSIS
Work of JAVAHERI et al.^[12]	2D monitor	×	DSIS
Work of JAVAHERI et al.^[27]	2D monitor	×	DSIS
Work of JAVAHERI et al.^[15]	2D monitor	×	DSIS
Work of DA SILVA CRUZ et al.^[16]	2D monitor	×	DSIS
Work of SU et al.^[18]	2D monitor	×	DSIS
IRPC^[19]	2D monitor	×	DSIS
WPC^[5]	2D monitor	×	DSIS
SJTU-PCQA^[6]	2D monitor	×	ACR
VsenseVVDB2^[20]	2D monitor	×	ACR
Work of CAO et al.^[21]	2D monitor	×	ACR
Work of ALEXIOU and EBRAHIMI^[8]	2D monitor	×	DSIS, ACR
VsenseVVDB^[17]	2D monitor	×	DSIS, PWC
Work of ZHANG et al.^[37]	2D monitor	×	-
Work of ALEXIOU et al.^[25]	2D monitor	√	DSIS
Work of ALEXIOU et al.^[22]	2D monitor	√	DSIS
LS-PCQA^[4]	2D monitor	√	DSIS
Work of TORLIG et al.^[10]	2D monitor	√	DSIS
M-PCCD^[11]	2D monitor	√	DSIS
Work of ALEXIOU et al.^[23]	2D monitor	√	DSIS, ACR
Work of ALEXIOU et al.^[24]	2D monitor	√	DSIS, ACR
Work of VIOLA et al.^[26]	2D monitor	-	DSIS
NBU-PCD 1.0^[28]	2D monitor	-	-
ICIP2020^[31]	2D/3D monitor	×	DSIS
RG-PCD^[30]	2D/3D monitor	×	DSIS
Work of ALEXIOU et al.^[29]	AR	√	DSIS
Work of NEHMÉ et al.^[9]	HMD	×	DSIS, ACR
PointXR^[35]	HMD	√	DSIS
SIAT-PCQD^[36]	HMD	√	DSIS
Work of SUBRAMANYAM et al.^[34]	HMD	√	ACR
Work of JESÚS GUTIÉRREZ et al.^[38]	HMD	√	ACR

Related Work	Display	Interaction	Methodology
Work of ALEXIOU et al.^[14]	2D monitor	×	DSIS
Work of ALEXIOU and EBRAHIMI^[13]	2D monitor	×	DSIS
Work of JAVAHERI et al.^[12]	2D monitor	×	DSIS
Work of JAVAHERI et al.^[27]	2D monitor	×	DSIS
Work of JAVAHERI et al.^[15]	2D monitor	×	DSIS
Work of DA SILVA CRUZ et al.^[16]	2D monitor	×	DSIS
Work of SU et al.^[18]	2D monitor	×	DSIS
IRPC^[19]	2D monitor	×	DSIS
WPC^[5]	2D monitor	×	DSIS
SJTU-PCQA^[6]	2D monitor	×	ACR
VsenseVVDB2^[20]	2D monitor	×	ACR
Work of CAO et al.^[21]	2D monitor	×	ACR
Work of ALEXIOU and EBRAHIMI^[8]	2D monitor	×	DSIS, ACR
VsenseVVDB^[17]	2D monitor	×	DSIS, PWC
Work of ZHANG et al.^[37]	2D monitor	×	-
Work of ALEXIOU et al.^[25]	2D monitor	√	DSIS
Work of ALEXIOU et al.^[22]	2D monitor	√	DSIS
LS-PCQA^[4]	2D monitor	√	DSIS
Work of TORLIG et al.^[10]	2D monitor	√	DSIS
M-PCCD^[11]	2D monitor	√	DSIS
Work of ALEXIOU et al.^[23]	2D monitor	√	DSIS, ACR
Work of ALEXIOU et al.^[24]	2D monitor	√	DSIS, ACR
Work of VIOLA et al.^[26]	2D monitor	-	DSIS
NBU-PCD 1.0^[28]	2D monitor	-	-
ICIP2020^[31]	2D/3D monitor	×	DSIS
RG-PCD^[30]	2D/3D monitor	×	DSIS
Work of ALEXIOU et al.^[29]	AR	√	DSIS
Work of NEHMÉ et al.^[9]	HMD	×	DSIS, ACR
PointXR^[35]	HMD	√	DSIS
SIAT-PCQD^[36]	HMD	√	DSIS
Work of SUBRAMANYAM et al.^[34]	HMD	√	ACR
Work of JESÚS GUTIÉRREZ et al.^[38]	HMD	√	ACR

Database	Year	Attribute	Models	Distortion Type
G-PCD^{[8, 29]}	2017	Colorless	40	Octree, Gaussian noise
RG-PCD^[30]	2018	Colorless	24	Octree
VsenseVVDB^[17]	2019	Colored	32	VPCC
M-PCCD^[11]	2019	Colored	244	GPCC, VPCC
IRPC^[19]	2020	Colorless& Colored	54 & 54	GPCC, VPCC
ICIP2020^[31]	2020	Colored	90	GPCC, VPCC
PointXR^[35]	2020	Colored	100	GPCC
NBU-PCD 1.0^[28]	2020	Colored	160	Octree
VsenseVVDB2^[20]	2020	Colored	164	Draco+JPEG, GPCC, VPCC
SJTU-PCQA^[6]	2020	Colored	420	Octree, downsampling, color and geometry noise
SIAT-PCQD^[36]	2021	Colored	340	VPCC
CPCD 2.0^[42]	2021	Colored	360	GPCC, VPCC, Gaussian noise
WPC^[5]	2021	Colored	740	Gaussian noise, downsampling, GPCC, VPCC
WPC2.0^[44]	2021	Colored	400	VPCC
WPC3.0^[45]	2022	Colored	350	VPCC
LS-PCQA^[4]	2023	Colored	1 080	Color and geometry noise, downsampling, GPCC, VPCC, etc.
BASICS^[43]	2023	Colored	1 494	VPCC, GPCC, GeoCNN^[46]

Database	Year	Attribute	Models	Distortion Type
G-PCD^{[8, 29]}	2017	Colorless	40	Octree, Gaussian noise
RG-PCD^[30]	2018	Colorless	24	Octree
VsenseVVDB^[17]	2019	Colored	32	VPCC
M-PCCD^[11]	2019	Colored	244	GPCC, VPCC
IRPC^[19]	2020	Colorless& Colored	54 & 54	GPCC, VPCC
ICIP2020^[31]	2020	Colored	90	GPCC, VPCC
PointXR^[35]	2020	Colored	100	GPCC
NBU-PCD 1.0^[28]	2020	Colored	160	Octree
VsenseVVDB2^[20]	2020	Colored	164	Draco+JPEG, GPCC, VPCC
SJTU-PCQA^[6]	2020	Colored	420	Octree, downsampling, color and geometry noise
SIAT-PCQD^[36]	2021	Colored	340	VPCC
CPCD 2.0^[42]	2021	Colored	360	GPCC, VPCC, Gaussian noise
WPC^[5]	2021	Colored	740	Gaussian noise, downsampling, GPCC, VPCC
WPC2.0^[44]	2021	Colored	400	VPCC
WPC3.0^[45]	2022	Colored	350	VPCC
LS-PCQA^[4]	2023	Colored	1 080	Color and geometry noise, downsampling, GPCC, VPCC, etc.
BASICS^[43]	2023	Colored	1 494	VPCC, GPCC, GeoCNN^[46]

Method	Reference Type	Feature Extraction	Handcrafted/Deep Learning
p2point^[68]	FR	Model-based	Handcrafted
p2plane^{[47, 49]}	FR	Model-based	Handcrafted
p2mesh^[69]	FR	Model-based	Handcrafted
Plane to plane^[23]	FR	Model-based	Handcrafted
PointSSIM^[52]	FR	Model-based	Handcrafted
GraphSIM^[55]	FR	Model-based	Handcrafted
MS-GraphSIM^[63]	FR	Model-based	Handcrafted
PCQM^[53]	FR	Model-based	Handcrafted
PC-MSDM^[50]	FR	Model-based	Handcrafted
Proposed by VIOLA et al.^[26]	FR	Model-based	Handcrafted
VQA-CPC^[28]	FR	Model-based	Handcrafted
CPC-GSCT^[42]	FR	Model-based	Handcrafted
Proposed by JAVAHERI et al.^[27]	FR	Model-based	Handcrafted
Proposed by JAVAHERI et al.^[51]	FR	Model-based	Handcrafted
Proposed by DINIZ et al.^[56]	FR	Model-based	Handcrafted
Proposed by DINIZ et al.^[57]	FR	Model-based	Handcrafted
Proposed by DINIZ et al.^[58]	FR	Model-based	Handcrafted
Proposed by DINIZ et al.^[59]	FR	Model-based	Handcrafted
Proposed by DINIZ et al.^[60]	FR	Model-based	Handcrafted
EPES^[62]	FR	Model-based	Handcrafted
PSNR_yuv^[10]	FR	Projection-based	Handcrafted
Proposed by WU et al.^[36]	FR	Projection-based	Handcrafted
Proposed by HE et al.^[65]	FR	Projection-based	Handcrafted
PB-PCQA^[6]	FR	Projection-based	Handcrafted
TGP-PCQA^[70]	FR	Projection-based	Handcrafted
Proposed by TU et al.^[71]	FR	Model & projection	Handcrafted
PCMRR^[72]	RR	Model-based	Handcrafted
R-PCQA^[73]	RR	Model-based	Handcrafted
RR-CAP^[74]	RR	Projection-based	Handcrafted
3D-NSS^[64]	NR	Model-based	Handcrafted
StreamPCQ^[75]	NR	Model-based	Handcrafted
Proposed by ZHOU et al.^[76]	NR	Model-based	Handcrafted
ResSCNN^[4]	NR	Model-based	Deep learning
PKT-PCQA^[77]	NR	Model-based	Deep learning
Proposed by TU et al.^[78]	NR	Projection-based	Deep learning
GPA-Net^[79]	NR	Projection-based	Deep learning
PQA-Net^[67]	NR	Projection-based	Deep learning
GMS-3DQA^[80]	NR	Projection-based	Deep learning
D³-PCQA^[81]	NR	Projection-based	Deep learning
PM-BVQA^[66]	NR	Projection-based	Deep learning
IT-PCQA^[82]	NR	Projection-based	Deep learning
3D-CNN-PCQA^[83]	NR	Projection-based	Deep learning
VQA-PC^[84]	NR	Projection-based	Deep learning
BQE-CVP^[61]	NR	Model & projection	Handcrafted
MM-PCQA^[85]	NR	Model & projection	Deep learning

Perceptual Quality Assessment for Point Clouds : A Survey

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 108

Related Articles 0

Recommended Articles

Metrics

Reference	Type	Methods	IRPC				CPCD2.0
Reference	Type	Methods	SRCC	PLCC	KRCC	RMSE	SRCC	PLCC	KRCC	RMSE
FR	Model-based	p2point_Hausdorff^[68]	0.212 5	0.238 8	0.145 5	0.960 1	0.314 5	0.348 2	0.217 9	1.099 5
		p2point_MSE^[68]	0.328 1	0.335 7	0.214 6	0.931 3	0.549 1	0.678 4	0.414 2	0.861 7
		p2plane_Hausdorff^{[47, 49]}	0.254 1	0.392 5	0.197 5	0.908 9	0.378 6	0.406 1	0.266 3	1.071 8
		p2plane_MSE^{[47, 49]}	0.256 4	0.429 6	0.195 7	0.892 8	0.569 2	0.691 4	0.438 5	0.847 4
		AS_MEAN^[23]	0.112 3	0.156 9	0.066 9	0.976 4	0.404 4	0.437 6	0.275 2	1.054 6
		AS_RMS^[23]	0.118 8	0.145 2	0.085 2	0.978 2	0.417 3	0.446 4	0.289 5	1.049 6
		AS_MSE^[23]	0.118 8	0.153 6	0.085 2	0.990 2	0.417 3	0.447 2	0.289 5	1.049 1
		PC-MSDM^[50]	0.151 9	0.272 9	0.106 3	0.951 5	0.532 1	0.625 4	0.384 2	0.915 2
		PCQM^[53]	0381 9	0.561 1	0.303 3	0.818 4	0.340 8	0.481 3	0.261 5	1.028 1
		CPC-GSCT^[42]	0.862 6	0.870 6	0.689 4	0.482 9	0.906 3	0.904 9	0.745 1	0.502 7
	Projection-based	PSNR^*	0.149 6	0.347 1	0.089 4	0.927 2	0.406 4	0.418 3	0.286 7	1.065 4
		SSIM^*[88]	0.080 6	0.238 5	0.048 6	0.960 1	0.534 7	0.564 7	0.379 2	0.968 0
		MS-SSIM^*[100]	0.116 4	0.328 0	0.069 7	0.934 0	0.568 6	0.621 2	0.414 0	0.919 2
		VIF^*[101]	0.171 6	0.094 9	0.121 7	0.984 2	0.674 4	0.698 5	0.495 7	0.839 4
		TGP-PCQA^[70]	0.650 0	0.800 5	0.555 6	0.491 4	0.906 6	0.909 4	0.758 9	0.489 2
NR	Model-based & Projection-based	BQE-CVP^[61]	0.729 8	0.726 5	0.542 7	0.658 6	0.789 0	0.795 0	0.598 3	0.721 8

Reference	Type	Method	SJTU-PCQA				WPC
Reference	Type	Method	SRCC	PLCC	KRCC	RMSE	SRCC	PLCC	KRCC	RMSE
FR	Model-based	p2point_Hausdorff^[68]	0.43	0.16	0.30	2.39	0.27	0.39	0.19	20.89
		p2point_MSE^[68]	0.40	0.47	0.28	2.13	0.45	0.48	0.31	19.89
		p2plane_Hausdorff^{[47, 49]}	0.46	0.37	0.33	2.44	0.28	0.27	0.16	21.98
		p2plane_MSE^{[47, 49]}	0.49	0.56	0.35	2.00	0.32	0.26	0.22	22.82
		AS_MEAN^[23]	0.51	0.65	0.36	1.82	-	-	-	-
		AS_RMS^[23]	0.52	0.65	0.37	1.82	-	-	-	-
		AS_MSE^[23]	0.52	0.65	0.37	1.82	-	-	-	-
		PC-MSDM^[50]	0.32	0.41	0.21	2.21	-	-	-	-
		PCQM^[53]	0.74	0.77	0.56	1.52	0.74	0.74	0.56	15.16
		GraphSIM^[55]	0.84	0.84	0.64	1.57	0.58	0.61	0.41	17.19
		PointSSIM^[52]	0.68	0.71	0.49	1.70	0.45	0.46	0.32	20.27
		CPC-GSCT^[42]	0.89	0.91	0.71	0.99	-	-	-	-
	Projection-based	PSNR_yuv^[10]	-	-	-	-	0.44	0.53	0.31	19.31
		PSNR^*	0.65	0.63	0.47	1.87	0.42	0.48	0.30	15.81
		SSIM^*[88]	0.55	0.56	0.39	1.99	0.38	0.49	0.32	15.77
		MS-SSIM^*[100]	0.72	0.74	0.52	1.62	-	-	-	-
		VIF^*[101]	0.74	0.78	0.54	1.49	-	-	-	-
		PB-PCQA^[6]	0.60	0.60	-	1.86	-	-	-	-
		TGP-PCQA^[70]	0.83	0.86	0.65	1.21	-	-	-	-
RR	Model-based	R-PCQA^[73]	-	-	-	-	0.88	0.88	-	-
	Model-based	PCMRR^[72]	0.48	0.61	0.33	1.93	0.30	0.34	0.20	21.53
	Projection-based	RR-CAP^[74]	0.75	0.76	0.55	1.55	0.71	0.73	0.52	15.64
NR	Model-based	3D-NSS^[64]	0.71	0.73	0.51	1.76	0.64	0.65	0.44	16.57
	Projection-based	BRISQUE^*[91]	0.20	0.22	0.11	2.24	0.37	0.41	0.24	22.54
		NIQE^*[96]	0.22	0.37	0.15	2.26	0.38	0.39	0.25	22.55
		IL-NIQE^*[93]	0.08	0.16	0.05	2.33	0.09	0.14	0.08	24.01
		VIIDEO^*[102]	0.05	0.29	0.04	2.31	0.07	0.08	0.05	22.92
		V-BLIINDS^*[103]	0.68	0.78	0.48	1.50	0.46	0.49	0.30	19.73
		TLVQM^*[104]	0.52	0.60	0.34	1.91	0.03	0.01	0.20	22.14
		VIDEVAL^*[105]	0.60	0.74	0.42	1.50	0.37	0.26	0.36	21.09
		VSFA^*[106]	0.72	0.82	0.54	1.40	0.63	0.63	0.46	17.23
		RAPIQUE^*[107]	0.44	0.40	0.34	2.21	0.27	0.35	0.20	21.14
		StairVQA^*[108]	0.79	0.78	0.55	1.42	0.72	0.71	0.52	15.07
		PQA-Net^[67]	-	-	-	-	0.69	0.70	0.51	15.18
		3D-CNN-PCQA^[83]	0.83	0.86	0.60	1.22	0.75	0.76	0.56	13.56
		ResSCNN^[4]	0.81	0.86	-	-	-	-	-	-
		IT-PCQA^[82]	0.63	0.58	-	-	0.54	0.55
		VQA-PC^[84]	0.85	0.86	0.65	1.13	0.79	0.79	0.61	13.62
	Model-based & projection-based	BQE-CVP^[61]	0.89	0.91	0.73	0.97	-	-	-	-

Reference	Type	Method	SJTU-PCQA				WPC
Reference	Type	Method	SRCC	PLCC	KRCC	RMSE	SRCC	PLCC	KRCC	RMSE
FR	Model-based	p2point_Hausdorff^[68]	0.43	0.16	0.30	2.39	0.27	0.39	0.19	20.89
		p2point_MSE^[68]	0.40	0.47	0.28	2.13	0.45	0.48	0.31	19.89
		p2plane_Hausdorff^{[47, 49]}	0.46	0.37	0.33	2.44	0.28	0.27	0.16	21.98
		p2plane_MSE^{[47, 49]}	0.49	0.56	0.35	2.00	0.32	0.26	0.22	22.82
		AS_MEAN^[23]	0.51	0.65	0.36	1.82	-	-	-	-
		AS_RMS^[23]	0.52	0.65	0.37	1.82	-	-	-	-
		AS_MSE^[23]	0.52	0.65	0.37	1.82	-	-	-	-
		PC-MSDM^[50]	0.32	0.41	0.21	2.21	-	-	-	-
		PCQM^[53]	0.74	0.77	0.56	1.52	0.74	0.74	0.56	15.16
		GraphSIM^[55]	0.84	0.84	0.64	1.57	0.58	0.61	0.41	17.19
		PointSSIM^[52]	0.68	0.71	0.49	1.70	0.45	0.46	0.32	20.27
		CPC-GSCT^[42]	0.89	0.91	0.71	0.99	-	-	-	-
	Projection-based	PSNR_yuv^[10]	-	-	-	-	0.44	0.53	0.31	19.31
		PSNR^*	0.65	0.63	0.47	1.87	0.42	0.48	0.30	15.81
		SSIM^*[88]	0.55	0.56	0.39	1.99	0.38	0.49	0.32	15.77
		MS-SSIM^*[100]	0.72	0.74	0.52	1.62	-	-	-	-
		VIF^*[101]	0.74	0.78	0.54	1.49	-	-	-	-
		PB-PCQA^[6]	0.60	0.60	-	1.86	-	-	-	-
		TGP-PCQA^[70]	0.83	0.86	0.65	1.21	-	-	-	-
RR	Model-based	R-PCQA^[73]	-	-	-	-	0.88	0.88	-	-
	Model-based	PCMRR^[72]	0.48	0.61	0.33	1.93	0.30	0.34	0.20	21.53
	Projection-based	RR-CAP^[74]	0.75	0.76	0.55	1.55	0.71	0.73	0.52	15.64
NR	Model-based	3D-NSS^[64]	0.71	0.73	0.51	1.76	0.64	0.65	0.44	16.57
	Projection-based	BRISQUE^*[91]	0.20	0.22	0.11	2.24	0.37	0.41	0.24	22.54
		NIQE^*[96]	0.22	0.37	0.15	2.26	0.38	0.39	0.25	22.55
		IL-NIQE^*[93]	0.08	0.16	0.05	2.33	0.09	0.14	0.08	24.01
		VIIDEO^*[102]	0.05	0.29	0.04	2.31	0.07	0.08	0.05	22.92
		V-BLIINDS^*[103]	0.68	0.78	0.48	1.50	0.46	0.49	0.30	19.73
		TLVQM^*[104]	0.52	0.60	0.34	1.91	0.03	0.01	0.20	22.14
		VIDEVAL^*[105]	0.60	0.74	0.42	1.50	0.37	0.26	0.36	21.09
		VSFA^*[106]	0.72	0.82	0.54	1.40	0.63	0.63	0.46	17.23
		RAPIQUE^*[107]	0.44	0.40	0.34	2.21	0.27	0.35	0.20	21.14
		StairVQA^*[108]	0.79	0.78	0.55	1.42	0.72	0.71	0.52	15.07
		PQA-Net^[67]	-	-	-	-	0.69	0.70	0.51	15.18
		3D-CNN-PCQA^[83]	0.83	0.86	0.60	1.22	0.75	0.76	0.56	13.56
		ResSCNN^[4]	0.81	0.86	-	-	-	-	-	-
		IT-PCQA^[82]	0.63	0.58	-	-	0.54	0.55
		VQA-PC^[84]	0.85	0.86	0.65	1.13	0.79	0.79	0.61	13.62
	Model-based & projection-based	BQE-CVP^[61]	0.89	0.91	0.73	0.97	-	-	-	-