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

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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:2023-12-25 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

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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

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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	-	-	-	-