High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MG-MVS		87.11 32	86.27 41	89.62 8	97.79 1	76.27 4	94.96 43	94.49 40	78.74 63	83.87 66	92.94 115	64.34 82	96.94 102	75.19 138	94.09 40	95.66 46

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 44	96.26 27	72.84 25	99.38 1	92.64 5	95.93 10	97.08 9

OPU-MVS				89.97 3	97.52 3	73.15 13	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

DVP-MVS++.		90.53 3	91.09 4	88.87 14	97.31 4	69.91 38	93.96 67	94.37 49	72.48 162	92.07 6	96.85 12	83.82 2	99.15 2	91.53 15	97.42 4	97.55 4

MSC_two_6792asdad				89.60 9	97.31 4	73.22 11		95.05 20					99.07 13	92.01 11	94.77 25	96.51 20

No_MVS				89.60 9	97.31 4	73.22 11		95.05 20					99.07 13	92.01 11	94.77 25	96.51 20

DP-MVS Recon		82.73 101	81.65 108	85.98 83	97.31 4	67.06 107	95.15 35	91.99 141	69.08 239	76.50 140	93.89 98	54.48 192	98.20 36	70.76 176	85.66 133	92.69 155

testtj		86.62 42	86.66 40	86.50 67	96.95 8	65.70 143	94.41 50	93.45 83	67.74 248	86.19 38	96.39 23	64.38 81	97.91 46	87.33 48	93.14 58	95.90 42

ETH3 D test6400		90.27 6	90.44 7	89.75 6	96.82 9	74.33 7	95.89 17	94.80 28	77.13 85	89.13 21	97.38 2	74.49 17	98.48 28	92.32 10	95.98 8	96.46 25

CNVR-MVS		90.32 5	90.89 5	88.61 19	96.76 10	70.65 25	96.47 12	94.83 25	84.83 8	89.07 22	96.80 15	70.86 32	99.06 15	92.64 5	95.71 11	96.12 34

ZD-MVS					96.63 11	65.50 150		93.50 81	70.74 218	85.26 52	95.19 56	64.92 77	97.29 77	87.51 44	93.01 59

NCCC		89.07 13	89.46 13	87.91 24	96.60 12	69.05 55	96.38 13	94.64 35	84.42 9	86.74 33	96.20 28	66.56 59	98.76 22	89.03 33	94.56 33	95.92 41

IU-MVS					96.46 13	69.91 38		95.18 13	80.75 37	95.28 1				92.34 7	95.36 14	96.47 24

SED-MVS		89.94 8	90.36 8	88.70 16	96.45 14	69.38 48	96.89 4	94.44 42	71.65 193	92.11 4	97.21 5	76.79 9	99.11 6	92.34 7	95.36 14	97.62 2

test_241102_ONE					96.45 14	69.38 48		94.44 42	71.65 193	92.11 4	97.05 8	76.79 9	99.11 6

test_0728_SECOND				88.70 16	96.45 14	70.43 28	96.64 8	94.37 49					99.15 2	91.91 13	94.90 21	96.51 20

DVP-MVS		89.41 11	89.73 12	88.45 21	96.40 17	69.99 34	96.64 8	94.52 38	71.92 179	90.55 15	96.93 10	73.77 20	99.08 11	91.91 13	94.90 21	96.29 30
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					96.40 17	69.99 34	96.76 6	94.33 51	71.92 179	91.89 8	97.11 7	73.77 20

AdaColmap		78.94 166	77.00 183	84.76 123	96.34 19	65.86 139	92.66 124	87.97 285	62.18 292	70.56 198	92.37 131	43.53 279	97.35 73	64.50 234	82.86 152	91.05 191

test_one_0601					96.32 20	69.74 43		94.18 55	71.42 205	90.67 14	96.85 12	74.45 18

test_part2					96.29 21	68.16 77				90.78 12

DPE-MVS		88.77 14	89.21 14	87.45 36	96.26 22	67.56 91	94.17 54	94.15 57	68.77 242	90.74 13	97.27 3	76.09 12	98.49 27	90.58 21	94.91 20	96.30 29
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MAR-MVS		84.18 78	83.43 78	86.44 69	96.25 23	65.93 138	94.28 53	94.27 53	74.41 118	79.16 110	95.61 40	53.99 197	98.88 20	69.62 185	93.26 56	94.50 96
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
API-MVS		82.28 108	80.53 124	87.54 34	96.13 24	70.59 26	93.63 86	91.04 186	65.72 266	75.45 150	92.83 121	56.11 173	98.89 19	64.10 236	89.75 101	93.15 143

APDe-MVS		87.54 24	87.84 21	86.65 60	96.07 25	66.30 129	94.84 45	93.78 64	69.35 233	88.39 24	96.34 25	67.74 48	97.66 58	90.62 20	93.44 54	96.01 38

PAPR		85.15 60	84.47 65	87.18 42	96.02 26	68.29 72	91.85 156	93.00 105	76.59 93	79.03 111	95.00 60	61.59 111	97.61 62	78.16 122	89.00 104	95.63 47

APD-MVS		85.93 50	85.99 46	85.76 92	95.98 27	65.21 156	93.59 88	92.58 121	66.54 259	86.17 39	95.88 33	63.83 88	97.00 94	86.39 56	92.94 60	95.06 72
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.48 2	87.95 19	88.00 19	87.79 27	95.86 28	68.32 71	95.74 21	94.11 59	83.82 11	83.49 68	96.19 29	64.53 80	98.44 30	83.42 78	94.88 24	96.61 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DP-MVS		69.90 269	66.48 275	80.14 238	95.36 29	62.93 216	89.56 231	76.11 344	50.27 342	57.69 311	85.23 221	39.68 291	95.73 143	33.35 353	71.05 235	81.78 319

114514_t		79.17 162	77.67 169	83.68 154	95.32 30	65.53 149	92.85 115	91.60 159	63.49 279	67.92 235	90.63 156	46.65 260	95.72 147	67.01 208	83.54 150	89.79 203

Regformer-1		87.24 29	87.60 27	86.15 80	95.14 31	65.83 141	93.95 70	95.12 15	82.11 21	84.25 59	95.73 36	67.88 46	98.35 33	85.60 60	88.64 108	94.26 101

Regformer-2		87.00 36	87.43 29	85.71 95	95.14 31	64.73 171	93.95 70	94.95 22	81.69 26	84.03 64	95.73 36	67.35 50	98.19 37	85.40 62	88.64 108	94.20 103

HPM-MVS++		89.37 12	89.95 11	87.64 29	95.10 33	68.23 76	95.24 32	94.49 40	82.43 17	88.90 23	96.35 24	71.89 31	98.63 24	88.76 35	96.40 6	96.06 35

CSCG		86.87 37	86.26 42	88.72 15	95.05 34	70.79 24	93.83 81	95.33 11	68.48 246	77.63 126	94.35 84	73.04 23	98.45 29	84.92 67	93.71 50	96.92 11

LFMVS		84.34 73	82.73 94	89.18 13	94.76 35	73.25 10	94.99 42	91.89 146	71.90 181	82.16 77	93.49 105	47.98 252	97.05 89	82.55 87	84.82 138	97.25 7

CDPH-MVS		85.71 53	85.46 55	86.46 68	94.75 36	67.19 103	93.89 75	92.83 110	70.90 213	83.09 71	95.28 48	63.62 92	97.36 72	80.63 102	94.18 39	94.84 81

test_prior3		87.38 27	87.70 24	86.42 70	94.71 37	67.35 98	95.10 37	93.10 101	75.40 106	85.25 53	95.61 40	67.94 43	96.84 106	87.47 45	94.77 25	95.05 73

test_prior				86.42 70	94.71 37	67.35 98		93.10 101					96.84 106			95.05 73

test12				87.09 45	94.60 39	68.86 59		92.91 107		82.67 75		65.44 70	97.55 63		93.69 51	94.84 81

test_yl		84.28 74	83.16 85	87.64 29	94.52 40	69.24 51	95.78 18	95.09 18	69.19 236	81.09 86	92.88 119	57.00 159	97.44 67	81.11 100	81.76 160	96.23 32

DCV-MVSNet		84.28 74	83.16 85	87.64 29	94.52 40	69.24 51	95.78 18	95.09 18	69.19 236	81.09 86	92.88 119	57.00 159	97.44 67	81.11 100	81.76 160	96.23 32

CANet		89.61 10	89.99 10	88.46 20	94.39 42	69.71 44	96.53 11	93.78 64	86.89 4	89.68 18	95.78 34	65.94 63	99.10 9	92.99 3	93.91 44	96.58 17

test_8					94.19 43	67.19 103	94.15 57	93.42 86	71.87 184	85.38 50	95.35 44	68.19 39	96.95 101

TEST9					94.18 44	67.28 100	94.16 55	93.51 79	71.75 191	85.52 48	95.33 45	68.01 42	97.27 81

train_agg		87.21 30	87.42 30	86.60 61	94.18 44	67.28 100	94.16 55	93.51 79	71.87 184	85.52 48	95.33 45	68.19 39	97.27 81	89.09 29	94.90 21	95.25 66

Regformer-3		85.80 52	85.92 47	85.46 102	94.17 46	65.09 164	92.95 111	95.11 16	81.13 33	81.68 80	95.04 58	65.82 65	98.32 34	83.02 80	84.36 142	92.97 150

Regformer-4		85.45 55	85.69 52	84.73 125	94.17 46	63.23 209	92.95 111	94.83 25	80.66 38	81.29 83	95.04 58	65.12 72	98.08 40	82.74 82	84.36 142	92.88 154

agg_prior1		87.02 35	87.26 32	86.28 77	94.16 48	66.97 111	94.08 61	93.31 89	71.85 186	84.49 57	95.39 43	68.91 36	96.75 110	88.84 34	94.32 38	95.13 69

agg_prior					94.16 48	66.97 111		93.31 89		84.49 57			96.75 110

PAPM_NR		82.97 98	81.84 105	86.37 73	94.10 50	66.76 118	87.66 266	92.84 109	69.96 226	74.07 161	93.57 103	63.10 101	97.50 65	70.66 178	90.58 94	94.85 79

FOURS1					93.95 51	61.77 237	93.96 67	91.92 144	62.14 293	86.57 34

VNet		86.20 46	85.65 53	87.84 26	93.92 52	69.99 34	95.73 23	95.94 6	78.43 65	86.00 42	93.07 112	58.22 145	97.00 94	85.22 64	84.33 145	96.52 19

9.14			87.63 25		93.86 53		94.41 50	94.18 55	72.76 156	86.21 37	96.51 19	66.64 57	97.88 49	90.08 23	94.04 41

xxxxxxxxxxxxxcwj		87.14 31	87.19 33	86.99 48	93.84 54	67.89 83	95.05 39	84.72 315	78.19 67	86.25 35	96.44 21	66.98 52	97.79 51	88.68 36	94.56 33	95.28 61

save fliter					93.84 54	67.89 83	95.05 39	92.66 116	78.19 67

PVSNet_BlendedMVS		83.38 92	83.43 78	83.22 165	93.76 56	67.53 93	94.06 62	93.61 75	79.13 55	81.00 89	85.14 222	63.19 99	97.29 77	87.08 51	73.91 214	84.83 283

PVSNet_Blended		86.73 41	86.86 38	86.31 76	93.76 56	67.53 93	96.33 15	93.61 75	82.34 18	81.00 89	93.08 110	63.19 99	97.29 77	87.08 51	91.38 83	94.13 109

HFP-MVS		84.73 66	84.40 68	85.72 93	93.75 58	65.01 165	93.50 92	93.19 95	72.19 173	79.22 108	94.93 64	59.04 139	97.67 55	81.55 93	92.21 69	94.49 97

#test#		84.98 63	84.74 64	85.72 93	93.75 58	65.01 165	94.09 60	93.19 95	73.55 141	79.22 108	94.93 64	59.04 139	97.67 55	82.66 84	92.21 69	94.49 97

Anonymous202405211		77.96 187	75.33 203	85.87 87	93.73 60	64.52 173	94.85 44	85.36 310	62.52 290	76.11 141	90.18 165	29.43 338	97.29 77	68.51 196	77.24 199	95.81 44

SD-MVS		87.49 26	87.49 28	87.50 35	93.60 61	68.82 61	93.90 74	92.63 119	76.86 89	87.90 26	95.76 35	66.17 60	97.63 60	89.06 31	91.48 81	96.05 36
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ETH3D-3000-0.1		87.61 23	87.89 20	86.75 55	93.58 62	67.21 102	94.31 52	94.14 58	72.92 153	87.13 29	96.62 17	67.81 47	97.94 43	90.13 22	94.42 36	95.09 71

ACMMPR		84.37 71	84.06 70	85.28 109	93.56 63	64.37 183	93.50 92	93.15 98	72.19 173	78.85 117	94.86 68	56.69 166	97.45 66	81.55 93	92.20 71	94.02 117

region2R		84.36 72	84.03 71	85.36 107	93.54 64	64.31 185	93.43 95	92.95 106	72.16 176	78.86 116	94.84 69	56.97 161	97.53 64	81.38 97	92.11 73	94.24 102

TSAR-MVS + GP.		87.96 18	88.37 17	86.70 59	93.51 65	65.32 152	95.15 35	93.84 63	78.17 69	85.93 43	94.80 70	75.80 13	98.21 35	89.38 25	88.78 105	96.59 15

PHI-MVS		86.83 39	86.85 39	86.78 54	93.47 66	65.55 148	95.39 29	95.10 17	71.77 190	85.69 47	96.52 18	62.07 108	98.77 21	86.06 58	95.60 12	96.03 37

SR-MVS		82.81 100	82.58 96	83.50 160	93.35 67	61.16 248	92.23 138	91.28 173	64.48 272	81.27 84	95.28 48	53.71 201	95.86 137	82.87 81	88.77 106	93.49 133

EPNet		87.84 21	88.38 16	86.23 78	93.30 68	66.05 133	95.26 31	94.84 24	87.09 3	88.06 25	94.53 75	66.79 56	97.34 74	83.89 75	91.68 77	95.29 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test1172		81.90 116	81.83 106	82.13 193	93.23 69	57.52 298	91.61 168	90.98 188	64.32 274	80.20 97	95.00 60	51.26 222	95.61 151	81.73 92	88.13 112	93.26 139

XVS		83.87 85	83.47 76	85.05 114	93.22 70	63.78 195	92.92 113	92.66 116	73.99 127	78.18 120	94.31 88	55.25 180	97.41 69	79.16 111	91.58 79	93.95 119

X-MVStestdata		76.86 201	74.13 221	85.05 114	93.22 70	63.78 195	92.92 113	92.66 116	73.99 127	78.18 120	10.19 371	55.25 180	97.41 69	79.16 111	91.58 79	93.95 119

SMA-MVS		88.14 15	88.29 18	87.67 28	93.21 72	68.72 63	93.85 77	94.03 60	74.18 124	91.74 9	96.67 16	65.61 68	98.42 32	89.24 28	96.08 7	95.88 43
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
原ACMM1				84.42 136	93.21 72	64.27 188		93.40 88	65.39 267	79.51 105	92.50 125	58.11 147	96.69 112	65.27 230	93.96 42	92.32 165

MVS_111021_HR		86.19 47	85.80 50	87.37 38	93.17 74	69.79 41	93.99 66	93.76 67	79.08 57	78.88 115	93.99 96	62.25 107	98.15 38	85.93 59	91.15 87	94.15 108

CP-MVS		83.71 89	83.40 81	84.65 129	93.14 75	63.84 193	94.59 47	92.28 128	71.03 211	77.41 129	94.92 66	55.21 183	96.19 123	81.32 98	90.70 92	93.91 121

DELS-MVS		90.05 7	90.09 9	89.94 4	93.14 75	73.88 8	97.01 3	94.40 47	88.32 2	85.71 46	94.91 67	74.11 19	98.91 17	87.26 50	95.94 9	97.03 10
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
ZNCC-MVS		85.33 58	85.08 59	86.06 81	93.09 77	65.65 145	93.89 75	93.41 87	73.75 135	79.94 99	94.68 73	60.61 120	98.03 41	82.63 86	93.72 49	94.52 94

DeepPCF-MVS	81.17 1	89.72 9	91.38 3	84.72 127	93.00 78	58.16 290	96.72 7	94.41 45	86.50 5	90.25 17	97.83 1	75.46 14	98.67 23	92.78 4	95.49 13	97.32 6

PLC	68.80 14	75.23 228	73.68 227	79.86 246	92.93 79	58.68 285	90.64 206	88.30 276	60.90 301	64.43 270	90.53 157	42.38 283	94.57 186	56.52 274	76.54 203	86.33 252
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DWT-MVSNet_test		83.95 83	82.80 92	87.41 37	92.90 80	70.07 33	89.12 244	94.42 44	82.15 20	77.64 125	91.77 140	70.81 33	96.22 122	65.03 231	81.36 164	95.94 39

MSP-MVS		90.38 4	91.87 1	85.88 86	92.83 81	64.03 192	93.06 104	94.33 51	82.19 19	93.65 3	96.15 30	85.89 1	97.19 83	91.02 19	97.75 1	96.43 26
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
mPP-MVS		82.96 99	82.44 99	84.52 133	92.83 81	62.92 218	92.76 116	91.85 148	71.52 201	75.61 148	94.24 90	53.48 205	96.99 97	78.97 114	90.73 91	93.64 130

GST-MVS		84.63 69	84.29 69	85.66 97	92.82 83	65.27 154	93.04 106	93.13 99	73.20 145	78.89 112	94.18 92	59.41 135	97.85 50	81.45 95	92.48 68	93.86 124

WTY-MVS		86.32 44	85.81 49	87.85 25	92.82 83	69.37 50	95.20 33	95.25 12	82.71 15	81.91 78	94.73 71	67.93 45	97.63 60	79.55 108	82.25 156	96.54 18

PGM-MVS		83.25 93	82.70 95	84.92 118	92.81 85	64.07 191	90.44 209	92.20 135	71.28 206	77.23 132	94.43 78	55.17 184	97.31 76	79.33 110	91.38 83	93.37 134

EI-MVSNet-Vis-set		83.77 87	83.67 73	84.06 144	92.79 86	63.56 205	91.76 161	94.81 27	79.65 47	77.87 122	94.09 93	63.35 97	97.90 47	79.35 109	79.36 175	90.74 193

SF-MVS		87.03 34	87.09 35	86.84 50	92.70 87	67.45 97	93.64 85	93.76 67	70.78 217	86.25 35	96.44 21	66.98 52	97.79 51	88.68 36	94.56 33	95.28 61

MVSTER		82.47 105	82.05 102	83.74 150	92.68 88	69.01 56	91.90 153	93.21 92	79.83 42	72.14 183	85.71 218	74.72 15	94.72 181	75.72 135	72.49 224	87.50 230

MP-MVS		85.02 61	84.97 61	85.17 113	92.60 89	64.27 188	93.24 99	92.27 129	73.13 147	79.63 104	94.43 78	61.90 109	97.17 84	85.00 65	92.56 65	94.06 114
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
thres200		79.66 153	78.33 157	83.66 156	92.54 90	65.82 142	93.06 104	96.31 3	74.90 115	73.30 166	88.66 179	59.67 131	95.61 151	47.84 307	78.67 182	89.56 208

test_part1		79.63 154	77.86 168	84.93 117	92.50 91	71.43 17	94.15 57	91.08 183	72.51 161	70.66 197	84.98 224	59.84 128	95.07 170	72.07 166	62.94 288	88.30 224

APD-MVS_3200maxsize		81.64 119	81.32 111	82.59 178	92.36 92	58.74 284	91.39 176	91.01 187	63.35 280	79.72 103	94.62 74	51.82 215	96.14 125	79.71 106	87.93 114	92.89 153

新几何1				84.73 125	92.32 93	64.28 187		91.46 166	59.56 311	79.77 102	92.90 117	56.95 162	96.57 115	63.40 240	92.91 61	93.34 135

1121		81.25 124	80.05 129	84.87 121	92.30 94	64.31 185	87.91 262	91.39 168	59.44 312	79.94 99	92.91 116	57.09 155	97.01 92	66.63 210	92.81 63	93.29 138

EI-MVSNet-UG-set		83.14 95	82.96 88	83.67 155	92.28 95	63.19 211	91.38 178	94.68 33	79.22 52	76.60 138	93.75 99	62.64 103	97.76 53	78.07 123	78.01 186	90.05 201

HPM-MVS		83.25 93	82.95 89	84.17 142	92.25 96	62.88 220	90.91 195	91.86 147	70.30 222	77.12 133	93.96 97	56.75 164	96.28 120	82.04 89	91.34 85	93.34 135
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HY-MVS	76.49 5	84.28 74	83.36 83	87.02 47	92.22 97	67.74 87	84.65 285	94.50 39	79.15 54	82.23 76	87.93 193	66.88 54	96.94 102	80.53 103	82.20 157	96.39 28

tfpn200view9		78.79 171	77.43 174	82.88 170	92.21 98	64.49 174	92.05 146	96.28 4	73.48 142	71.75 189	88.26 187	60.07 126	95.32 164	45.16 317	77.58 191	88.83 211

thres400		78.68 174	77.43 174	82.43 180	92.21 98	64.49 174	92.05 146	96.28 4	73.48 142	71.75 189	88.26 187	60.07 126	95.32 164	45.16 317	77.58 191	87.48 231

PS-MVSNAJ		88.14 15	87.61 26	89.71 7	92.06 100	76.72 1	95.75 20	93.26 91	83.86 10	89.55 19	96.06 31	53.55 202	97.89 48	91.10 17	93.31 55	94.54 92

SR-MVS-dyc-post		81.06 129	80.70 120	82.15 191	92.02 101	58.56 286	90.90 196	90.45 198	62.76 286	78.89 112	94.46 76	51.26 222	95.61 151	78.77 117	86.77 124	92.28 167

RE-MVS-def			80.48 125		92.02 101	58.56 286	90.90 196	90.45 198	62.76 286	78.89 112	94.46 76	49.30 239		78.77 117	86.77 124	92.28 167

MSLP-MVS++		86.27 45	85.91 48	87.35 39	92.01 103	68.97 58	95.04 41	92.70 113	79.04 58	81.50 82	96.50 20	58.98 141	96.78 108	83.49 77	93.93 43	96.29 30

ETH3D cwj APD-0.16		87.06 33	87.18 34	86.71 57	91.99 104	67.48 96	92.97 109	94.21 54	71.48 204	85.72 45	96.32 26	68.13 41	98.00 42	89.06 31	94.70 31	94.65 88

旧先验1					91.94 105	60.74 258		91.50 164			94.36 80	65.23 71			91.84 74	94.55 90

thres600view7		78.00 185	76.66 187	82.03 199	91.93 106	63.69 200	91.30 183	96.33 1	72.43 165	70.46 200	87.89 194	60.31 121	94.92 176	42.64 329	76.64 202	87.48 231

LS3D		69.17 273	66.40 276	77.50 277	91.92 107	56.12 309	85.12 282	80.37 338	46.96 349	56.50 315	87.51 199	37.25 308	93.71 223	32.52 358	79.40 174	82.68 309

GG-mvs-BLEND				86.53 66	91.91 108	69.67 46	75.02 337	94.75 30		78.67 119	90.85 152	77.91 7	94.56 188	72.25 162	93.74 48	95.36 54

thres100view900		78.37 180	77.01 182	82.46 179	91.89 109	63.21 210	91.19 189	96.33 1	72.28 170	70.45 201	87.89 194	60.31 121	95.32 164	45.16 317	77.58 191	88.83 211

zzz-MVS		84.73 66	84.47 65	85.50 100	91.89 109	65.16 158	91.55 169	92.23 130	75.32 108	80.53 93	95.21 54	56.06 174	97.16 85	84.86 68	92.55 66	94.18 104

MTAPA		83.91 84	83.38 82	85.50 100	91.89 109	65.16 158	81.75 305	92.23 130	75.32 108	80.53 93	95.21 54	56.06 174	97.16 85	84.86 68	92.55 66	94.18 104

RRT_test8_iter05		80.61 135	79.62 138	83.60 157	91.87 112	66.90 113	93.42 97	93.68 72	77.09 87	68.83 224	85.63 219	66.82 55	95.42 160	76.46 133	62.74 290	88.48 218

canonicalmvs		86.85 38	86.25 43	88.66 18	91.80 113	71.92 15	93.54 90	91.71 154	80.26 41	87.55 27	95.25 52	63.59 94	96.93 104	88.18 38	84.34 144	97.11 8

TSAR-MVS + MP.		88.11 17	88.64 15	86.54 65	91.73 114	68.04 79	90.36 213	93.55 78	82.89 13	91.29 11	92.89 118	72.27 28	96.03 132	87.99 39	94.77 25	95.54 50
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		81.49 120	80.67 121	83.93 147	91.71 115	62.90 219	92.13 140	92.22 134	71.79 189	71.68 191	93.49 105	50.32 228	96.96 100	78.47 119	84.22 149	91.93 174
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
BH-RMVSNet		79.46 159	77.65 170	84.89 119	91.68 116	65.66 144	93.55 89	88.09 282	72.93 152	73.37 165	91.12 149	46.20 267	96.12 126	56.28 276	85.61 134	92.91 152

baseline1		81.84 117	81.03 117	84.28 141	91.60 117	66.62 121	91.08 192	91.66 157	81.87 24	74.86 153	91.67 144	69.98 35	94.92 176	71.76 169	64.75 276	91.29 188

ACMMP_NAP		86.05 48	85.80 50	86.80 53	91.58 118	67.53 93	91.79 158	93.49 82	74.93 114	84.61 55	95.30 47	59.42 134	97.92 45	86.13 57	94.92 19	94.94 78

MVS_Test		84.16 79	83.20 84	87.05 46	91.56 119	69.82 40	89.99 226	92.05 138	77.77 76	82.84 72	86.57 208	63.93 87	96.09 127	74.91 144	89.18 103	95.25 66

HPM-MVS_fast		80.25 142	79.55 142	82.33 184	91.55 120	59.95 270	91.32 182	89.16 250	65.23 270	74.71 154	93.07 112	47.81 254	95.74 142	74.87 146	88.23 110	91.31 187

CPTT-MVS		79.59 155	79.16 150	80.89 228	91.54 121	59.80 272	92.10 142	88.54 273	60.42 304	72.96 168	93.28 107	48.27 248	92.80 246	78.89 116	86.50 129	90.06 200

CNLPA		74.31 236	72.30 242	80.32 233	91.49 122	61.66 240	90.85 199	80.72 337	56.67 326	63.85 274	90.64 154	46.75 259	90.84 290	53.79 284	75.99 206	88.47 220

MP-MVS-pluss		85.24 59	85.13 58	85.56 99	91.42 123	65.59 147	91.54 170	92.51 123	74.56 117	80.62 92	95.64 39	59.15 138	97.00 94	86.94 53	93.80 46	94.07 113
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
gg-mvs-nofinetune		77.18 199	74.31 216	85.80 90	91.42 123	68.36 70	71.78 339	94.72 31	49.61 343	77.12 133	45.92 359	77.41 8	93.98 215	67.62 203	93.16 57	95.05 73

xiu_mvs_v2_base		87.92 20	87.38 31	89.55 12	91.41 125	76.43 3	95.74 21	93.12 100	83.53 12	89.55 19	95.95 32	53.45 206	97.68 54	91.07 18	92.62 64	94.54 92

EIA-MVS		84.84 65	84.88 62	84.69 128	91.30 126	62.36 227	93.85 77	92.04 139	79.45 48	79.33 107	94.28 89	62.42 106	96.35 119	80.05 105	91.25 86	95.38 53

alignmvs		87.28 28	86.97 36	88.24 23	91.30 126	71.14 22	95.61 25	93.56 77	79.30 50	87.07 32	95.25 52	68.43 37	96.93 104	87.87 40	84.33 145	96.65 13

EPMVS		78.49 179	75.98 196	86.02 82	91.21 128	69.68 45	80.23 318	91.20 174	75.25 110	72.48 178	78.11 302	54.65 188	93.69 224	57.66 272	83.04 151	94.69 84

FMVSNet3		77.73 191	76.04 195	82.80 171	91.20 129	68.99 57	91.87 154	91.99 141	73.35 144	67.04 248	83.19 244	56.62 167	92.14 269	59.80 264	69.34 242	87.28 237

Anonymous20240529		76.84 203	74.15 220	84.88 120	91.02 130	64.95 168	93.84 80	91.09 181	53.57 333	73.00 167	87.42 200	35.91 317	97.32 75	69.14 191	72.41 226	92.36 163

tpmvs		72.88 250	69.76 262	82.22 189	90.98 131	67.05 108	78.22 330	88.30 276	63.10 284	64.35 271	74.98 324	55.09 185	94.27 199	43.25 323	69.57 241	85.34 278

MVS		84.66 68	82.86 91	90.06 2	90.93 132	74.56 6	87.91 262	95.54 9	68.55 244	72.35 182	94.71 72	59.78 130	98.90 18	81.29 99	94.69 32	96.74 12

PVSNet	73.49 8	80.05 146	78.63 154	84.31 139	90.92 133	64.97 167	92.47 132	91.05 185	79.18 53	72.43 180	90.51 158	37.05 313	94.06 208	68.06 198	86.00 131	93.90 123

RRT_MVS		77.38 196	76.59 188	79.77 249	90.91 134	63.61 204	91.15 190	90.91 189	72.28 170	72.06 185	87.28 203	43.92 277	89.04 309	73.32 150	67.47 257	86.67 245

3Dnovator+	73.60 7	82.10 113	80.60 123	86.60 61	90.89 135	66.80 117	95.20 33	93.44 85	74.05 126	67.42 243	92.49 127	49.46 237	97.65 59	70.80 175	91.68 77	95.33 55

VDD-MVS		83.06 96	81.81 107	86.81 52	90.86 136	67.70 88	95.40 28	91.50 164	75.46 103	81.78 79	92.34 132	40.09 290	97.13 87	86.85 54	82.04 158	95.60 48

BH-w/o		80.49 139	79.30 147	84.05 145	90.83 137	64.36 184	93.60 87	89.42 240	74.35 120	69.09 217	90.15 166	55.23 182	95.61 151	64.61 233	86.43 130	92.17 172

ET-MVSNet_ETH3D		84.01 81	83.15 87	86.58 63	90.78 138	70.89 23	94.74 46	94.62 36	81.44 29	58.19 305	93.64 101	73.64 22	92.35 266	82.66 84	78.66 183	96.50 23

CS-MVS-test		85.35 57	85.55 54	84.75 124	90.77 139	65.29 153	95.38 30	91.54 160	78.03 71	83.67 67	94.32 87	62.47 105	95.81 138	82.73 83	91.00 89	93.15 143

Anonymous20231211		73.08 245	70.39 256	81.13 218	90.62 140	63.33 207	91.40 174	90.06 220	51.84 337	64.46 269	80.67 279	36.49 315	94.07 207	63.83 238	64.17 281	85.98 264

TR-MVS		78.77 172	77.37 179	82.95 169	90.49 141	60.88 252	93.67 84	90.07 217	70.08 225	74.51 155	91.37 147	45.69 268	95.70 148	60.12 262	80.32 170	92.29 166

CS-MVS		87.54 24	87.81 22	86.74 56	90.46 142	70.23 31	96.34 14	92.31 127	81.40 30	86.14 40	95.17 57	65.49 69	95.92 135	89.09 29	93.91 44	94.06 114

SteuartSystems-ACMMP		86.82 40	86.90 37	86.58 63	90.42 143	66.38 126	96.09 16	93.87 62	77.73 77	84.01 65	95.66 38	63.39 96	97.94 43	87.40 47	93.55 53	95.42 51
Skip Steuart: Steuart Systems R&D Blog.
TAPA-MVS	70.22 12	74.94 232	73.53 228	79.17 260	90.40 144	52.07 328	89.19 242	89.61 235	62.69 288	70.07 206	92.67 123	48.89 246	94.32 196	38.26 343	79.97 171	91.12 190
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_anonymous		81.36 122	79.99 132	85.46 102	90.39 145	68.40 69	86.88 276	90.61 197	74.41 118	70.31 204	84.67 228	63.79 89	92.32 267	73.13 151	85.70 132	95.67 45

CANet_DTU		84.09 80	83.52 74	85.81 89	90.30 146	66.82 115	91.87 154	89.01 258	85.27 6	86.09 41	93.74 100	47.71 255	96.98 98	77.90 125	89.78 100	93.65 129

Fast-Effi-MVS+		81.14 126	80.01 131	84.51 134	90.24 147	65.86 139	94.12 59	89.15 251	73.81 134	75.37 151	88.26 187	57.26 153	94.53 191	66.97 209	84.92 137	93.15 143

ETV-MVS		86.01 49	86.11 45	85.70 96	90.21 148	67.02 110	93.43 95	91.92 144	81.21 32	84.13 63	94.07 95	60.93 117	95.63 149	89.28 27	89.81 98	94.46 99

tpmrst		80.57 136	79.14 151	84.84 122	90.10 149	68.28 73	81.70 306	89.72 233	77.63 80	75.96 142	79.54 294	64.94 76	92.71 249	75.43 136	77.28 198	93.55 131

PVSNet_Blended_VisFu		83.97 82	83.50 75	85.39 106	90.02 150	66.59 123	93.77 82	91.73 152	77.43 84	77.08 135	89.81 171	63.77 90	96.97 99	79.67 107	88.21 111	92.60 158

UGNet		79.87 150	78.68 153	83.45 162	89.96 151	61.51 242	92.13 140	90.79 191	76.83 90	78.85 117	86.33 211	38.16 299	96.17 124	67.93 200	87.17 119	92.67 156
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
CHOSEN 1792x2688		84.98 63	83.45 77	89.57 11	89.94 152	75.14 5	92.07 145	92.32 126	81.87 24	75.68 145	88.27 186	60.18 123	98.60 25	80.46 104	90.27 97	94.96 77

abl_6		79.82 151	79.20 149	81.70 205	89.85 153	58.34 288	88.47 255	90.07 217	62.56 289	77.71 124	93.08 110	47.65 256	96.78 108	77.94 124	85.45 135	89.99 202

BH-untuned		78.68 174	77.08 180	83.48 161	89.84 154	63.74 197	92.70 120	88.59 271	71.57 199	66.83 251	88.65 180	51.75 217	95.39 162	59.03 267	84.77 139	91.32 186

test222					89.77 155	61.60 241	89.55 232	89.42 240	56.83 325	77.28 131	92.43 129	52.76 209			91.14 88	93.09 146

PMMVS		81.98 115	82.04 103	81.78 201	89.76 156	56.17 308	91.13 191	90.69 193	77.96 72	80.09 98	93.57 103	46.33 265	94.99 172	81.41 96	87.46 117	94.17 106

DPM-MVS		90.70 2	90.52 6	91.24 1	89.68 157	76.68 2	97.29 1	95.35 10	82.87 14	91.58 10	97.22 4	79.93 5	99.10 9	83.12 79	97.64 2	97.94 1

QAPM		79.95 149	77.39 178	87.64 29	89.63 158	71.41 18	93.30 98	93.70 71	65.34 269	67.39 245	91.75 142	47.83 253	98.96 16	57.71 271	89.81 98	92.54 160

3Dnovator	73.91 6	82.69 104	80.82 118	88.31 22	89.57 159	71.26 19	92.60 126	94.39 48	78.84 60	67.89 237	92.48 128	48.42 247	98.52 26	68.80 195	94.40 37	95.15 68

Effi-MVS+		83.82 86	82.76 93	86.99 48	89.56 160	69.40 47	91.35 180	86.12 304	72.59 158	83.22 70	92.81 122	59.60 132	96.01 134	81.76 91	87.80 115	95.56 49

PatchmatchNet		77.46 194	74.63 209	85.96 84	89.55 161	70.35 29	79.97 322	89.55 236	72.23 172	70.94 195	76.91 313	57.03 157	92.79 247	54.27 282	81.17 165	94.74 83
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.06 256	69.98 257	78.28 269	89.51 162	55.70 312	83.49 292	83.39 329	61.24 300	63.72 275	82.76 246	34.77 320	93.03 235	53.37 287	77.59 190	86.12 261

thisisatest0515		83.41 91	82.49 98	86.16 79	89.46 163	68.26 74	93.54 90	94.70 32	74.31 121	75.75 143	90.92 150	72.62 26	96.52 117	69.64 183	81.50 162	93.71 127

h-mvs33		83.01 97	82.56 97	84.35 138	89.34 164	62.02 232	92.72 118	93.76 67	81.45 27	82.73 73	92.25 134	60.11 124	97.13 87	87.69 42	62.96 287	93.91 121

DROMVSNet		84.53 70	85.04 60	83.01 168	89.34 164	61.37 245	94.42 49	91.09 181	77.91 74	83.24 69	94.20 91	58.37 144	95.40 161	85.35 63	91.41 82	92.27 170

UA-Net		80.02 147	79.65 137	81.11 219	89.33 166	57.72 294	86.33 279	89.00 259	77.44 83	81.01 88	89.15 176	59.33 136	95.90 136	61.01 256	84.28 147	89.73 205

dp		75.01 231	72.09 244	83.76 149	89.28 167	66.22 132	79.96 323	89.75 228	71.16 208	67.80 239	77.19 310	51.81 216	92.54 257	50.39 293	71.44 233	92.51 161

sss		82.71 103	82.38 100	83.73 152	89.25 168	59.58 275	92.24 137	94.89 23	77.96 72	79.86 101	92.38 130	56.70 165	97.05 89	77.26 128	80.86 168	94.55 90

MVSFormer		83.75 88	82.88 90	86.37 73	89.24 169	71.18 20	89.07 245	90.69 193	65.80 264	87.13 29	94.34 85	64.99 74	92.67 252	72.83 154	91.80 75	95.27 63

lupinMVS		87.74 22	87.77 23	87.63 33	89.24 169	71.18 20	96.57 10	92.90 108	82.70 16	87.13 29	95.27 50	64.99 74	95.80 139	89.34 26	91.80 75	95.93 40

IB-MVS	77.80 4	82.18 109	80.46 126	87.35 39	89.14 171	70.28 30	95.59 26	95.17 14	78.85 59	70.19 205	85.82 216	70.66 34	97.67 55	72.19 165	66.52 263	94.09 111
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MDTV_nov1_ep13			72.61 238		89.06 172	68.48 67	80.33 316	90.11 216	71.84 187	71.81 188	75.92 321	53.01 208	93.92 218	48.04 304	73.38 216

testdata				81.34 211	89.02 173	57.72 294		89.84 226	58.65 316	85.32 51	94.09 93	57.03 157	93.28 231	69.34 188	90.56 95	93.03 148

CostFormer		82.33 107	81.15 112	85.86 88	89.01 174	68.46 68	82.39 303	93.01 103	75.59 101	80.25 96	81.57 263	72.03 30	94.96 173	79.06 113	77.48 195	94.16 107

GeoE		78.90 167	77.43 174	83.29 164	88.95 175	62.02 232	92.31 134	86.23 302	70.24 223	71.34 194	89.27 174	54.43 193	94.04 211	63.31 241	80.81 169	93.81 126

GBi-Net		75.65 222	73.83 225	81.10 220	88.85 176	65.11 161	90.01 223	90.32 204	70.84 214	67.04 248	80.25 286	48.03 249	91.54 283	59.80 264	69.34 242	86.64 246

test1		75.65 222	73.83 225	81.10 220	88.85 176	65.11 161	90.01 223	90.32 204	70.84 214	67.04 248	80.25 286	48.03 249	91.54 283	59.80 264	69.34 242	86.64 246

FMVSNet2		76.07 211	74.01 223	82.26 188	88.85 176	67.66 89	91.33 181	91.61 158	70.84 214	65.98 255	82.25 252	48.03 249	92.00 274	58.46 269	68.73 248	87.10 239

DeepC-MVS	77.85 3	85.52 54	85.24 57	86.37 73	88.80 179	66.64 120	92.15 139	93.68 72	81.07 34	76.91 136	93.64 101	62.59 104	98.44 30	85.50 61	92.84 62	94.03 116
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		81.79 118	81.52 109	82.61 177	88.77 180	60.21 267	93.02 108	93.66 74	68.52 245	72.90 170	90.39 161	72.19 29	94.96 173	74.93 143	79.29 177	92.67 156

1112_ss		80.56 137	79.83 135	82.77 172	88.65 181	60.78 254	92.29 135	88.36 275	72.58 159	72.46 179	94.95 62	65.09 73	93.42 230	66.38 216	77.71 188	94.10 110

tpm cat1		75.30 227	72.21 243	84.58 132	88.52 182	67.77 86	78.16 331	88.02 283	61.88 297	68.45 231	76.37 317	60.65 118	94.03 213	53.77 285	74.11 211	91.93 174

LCM-MVSNet-Re		72.93 248	71.84 246	76.18 293	88.49 183	48.02 345	80.07 321	70.17 357	73.96 130	52.25 327	80.09 289	49.98 232	88.24 315	67.35 204	84.23 148	92.28 167

Vis-MVSNet		80.92 132	79.98 133	83.74 150	88.48 184	61.80 236	93.44 94	88.26 280	73.96 130	77.73 123	91.76 141	49.94 233	94.76 178	65.84 222	90.37 96	94.65 88
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Vis-MVSNet (Re-imp)		79.24 161	79.57 139	78.24 271	88.46 185	52.29 327	90.41 211	89.12 253	74.24 123	69.13 216	91.91 138	65.77 66	90.09 302	59.00 268	88.09 113	92.33 164

ab-mvs		80.18 143	78.31 158	85.80 90	88.44 186	65.49 151	83.00 300	92.67 115	71.82 188	77.36 130	85.01 223	54.50 189	96.59 113	76.35 134	75.63 207	95.32 57

gm-plane-assit					88.42 187	67.04 109			78.62 64		91.83 139		97.37 71	76.57 131

MVS_111021_LR		82.02 114	81.52 109	83.51 159	88.42 187	62.88 220	89.77 229	88.93 260	76.78 91	75.55 149	93.10 108	50.31 229	95.38 163	83.82 76	87.02 120	92.26 171

ECVR-MVS1		81.29 123	80.38 127	84.01 146	88.39 189	61.96 234	92.56 131	86.79 296	77.66 79	76.63 137	91.42 145	46.34 264	95.24 168	74.36 148	89.23 102	94.85 79

baseline		85.01 62	84.44 67	86.71 57	88.33 190	68.73 62	90.24 217	91.82 150	81.05 35	81.18 85	92.50 125	63.69 91	96.08 129	84.45 70	86.71 126	95.32 57

tpm2		79.80 152	77.95 165	85.34 108	88.28 191	68.26 74	81.56 308	91.42 167	70.11 224	77.59 128	80.50 281	67.40 49	94.26 201	67.34 205	77.35 196	93.51 132

thisisatest0530		81.15 125	80.07 128	84.39 137	88.26 192	65.63 146	91.40 174	94.62 36	71.27 207	70.93 196	89.18 175	72.47 27	96.04 131	65.62 225	76.89 201	91.49 179

casdiffmvs		85.37 56	84.87 63	86.84 50	88.25 193	69.07 54	93.04 106	91.76 151	81.27 31	80.84 91	92.07 136	64.23 83	96.06 130	84.98 66	87.43 118	95.39 52

Test_1112_low_res		79.56 156	78.60 155	82.43 180	88.24 194	60.39 264	92.09 143	87.99 284	72.10 177	71.84 187	87.42 200	64.62 79	93.04 234	65.80 223	77.30 197	93.85 125

PAPM		85.89 51	85.46 55	87.18 42	88.20 195	72.42 14	92.41 133	92.77 111	82.11 21	80.34 95	93.07 112	68.27 38	95.02 171	78.39 120	93.59 52	94.09 111

TESTMET0.1,1		82.41 106	81.98 104	83.72 153	88.08 196	63.74 197	92.70 120	93.77 66	79.30 50	77.61 127	87.57 198	58.19 146	94.08 206	73.91 149	86.68 127	93.33 137

ADS-MVSNet2		66.90 292	63.44 297	77.26 283	88.06 197	60.70 259	68.01 348	75.56 347	57.57 318	64.48 267	69.87 341	38.68 293	84.10 336	40.87 334	67.89 254	86.97 240

ADS-MVSNet		68.54 280	64.38 293	81.03 224	88.06 197	66.90 113	68.01 348	84.02 322	57.57 318	64.48 267	69.87 341	38.68 293	89.21 308	40.87 334	67.89 254	86.97 240

EPNet_dtu		78.80 170	79.26 148	77.43 279	88.06 197	49.71 340	91.96 152	91.95 143	77.67 78	76.56 139	91.28 148	58.51 143	90.20 300	56.37 275	80.95 167	92.39 162
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_enhance_ethall		78.86 168	77.97 164	81.54 207	88.00 200	65.17 157	91.41 172	89.15 251	75.19 111	68.79 225	83.98 235	67.17 51	92.82 244	72.73 157	65.30 267	86.62 250

IS-MVSNet		80.14 144	79.41 144	82.33 184	87.91 201	60.08 269	91.97 151	88.27 278	72.90 154	71.44 193	91.73 143	61.44 112	93.66 225	62.47 249	86.53 128	93.24 140

CLD-MVS		82.73 101	82.35 101	83.86 148	87.90 202	67.65 90	95.45 27	92.18 137	85.06 7	72.58 175	92.27 133	52.46 212	95.78 140	84.18 71	79.06 178	88.16 225
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ECVR-MVS		80.84 133	80.02 130	83.33 163	87.87 203	60.76 256	92.62 125	86.86 295	77.86 75	75.73 144	91.39 146	46.35 263	94.70 184	72.79 156	88.68 107	94.52 94

HyFIR lowres test		81.03 130	79.56 140	85.43 104	87.81 204	68.11 78	90.18 218	90.01 222	70.65 219	72.95 169	86.06 214	63.61 93	94.50 193	75.01 142	79.75 173	93.67 128

1314		80.70 134	78.95 152	85.94 85	87.77 205	67.56 91	87.91 262	92.55 122	72.17 175	67.44 242	93.09 109	50.27 230	97.04 91	71.68 171	87.64 116	93.23 141

cl22		77.94 188	76.78 185	81.42 209	87.57 206	64.93 169	90.67 204	88.86 263	72.45 164	67.63 241	82.68 248	64.07 84	92.91 242	71.79 167	65.30 267	86.44 251

HQP-NCC					87.54 207		94.06 62		79.80 43	74.18 157

ACMP_Plane					87.54 207		94.06 62		79.80 43	74.18 157

HQP-MVS		81.14 126	80.64 122	82.64 176	87.54 207	63.66 202	94.06 62	91.70 155	79.80 43	74.18 157	90.30 163	51.63 219	95.61 151	77.63 126	78.90 179	88.63 215

NP-MVS					87.41 210	63.04 212					90.30 163

diffmvs		84.28 74	83.83 72	85.61 98	87.40 211	68.02 80	90.88 198	89.24 245	80.54 39	81.64 81	92.52 124	59.83 129	94.52 192	87.32 49	85.11 136	94.29 100

baseline2		83.68 90	83.42 80	84.48 135	87.37 212	66.00 135	90.06 221	95.93 7	79.71 46	69.08 218	90.39 161	77.92 6	96.28 120	78.91 115	81.38 163	91.16 189

plane_prior6					87.23 213	62.32 228						50.66 226

tttt0517		79.50 157	78.53 156	82.41 183	87.22 214	61.43 244	89.75 230	94.76 29	69.29 234	67.91 236	88.06 192	72.92 24	95.63 149	62.91 245	73.90 215	90.16 199

plane_prior1					87.15 215

cascas		78.18 183	75.77 199	85.41 105	87.14 216	69.11 53	92.96 110	91.15 178	66.71 258	70.47 199	86.07 213	37.49 307	96.48 118	70.15 181	79.80 172	90.65 194

CHOSEN 280x420		77.35 197	76.95 184	78.55 266	87.07 217	62.68 224	69.71 344	82.95 331	68.80 241	71.48 192	87.27 204	66.03 62	84.00 339	76.47 132	82.81 154	88.95 210

HQP_MVS		80.34 141	79.75 136	82.12 194	86.94 218	62.42 225	93.13 102	91.31 170	78.81 61	72.53 176	89.14 177	50.66 226	95.55 157	76.74 129	78.53 184	88.39 221

plane_prior7					86.94 218	61.51 242

test-LLR		80.10 145	79.56 140	81.72 203	86.93 220	61.17 246	92.70 120	91.54 160	71.51 202	75.62 146	86.94 205	53.83 198	92.38 263	72.21 163	84.76 140	91.60 177

test-mter		79.96 148	79.38 146	81.72 203	86.93 220	61.17 246	92.70 120	91.54 160	73.85 132	75.62 146	86.94 205	49.84 235	92.38 263	72.21 163	84.76 140	91.60 177

SCA		75.82 220	72.76 235	85.01 116	86.63 222	70.08 32	81.06 312	89.19 248	71.60 198	70.01 207	77.09 311	45.53 269	90.25 295	60.43 259	73.27 217	94.68 85

AUN-MVS		78.37 180	77.43 174	81.17 215	86.60 223	57.45 300	89.46 236	91.16 176	74.11 125	74.40 156	90.49 159	55.52 179	94.57 186	74.73 147	60.43 313	91.48 180

hse-mvs2		81.12 128	81.11 116	81.16 216	86.52 224	57.48 299	89.40 237	91.16 176	81.45 27	82.73 73	90.49 159	60.11 124	94.58 185	87.69 42	60.41 314	91.41 182

xiu_mvs_v1_base_debu		82.16 110	81.12 113	85.26 110	86.42 225	68.72 63	92.59 128	90.44 201	73.12 148	84.20 60	94.36 80	38.04 301	95.73 143	84.12 72	86.81 121	91.33 183

xiu_mvs_v1_base		82.16 110	81.12 113	85.26 110	86.42 225	68.72 63	92.59 128	90.44 201	73.12 148	84.20 60	94.36 80	38.04 301	95.73 143	84.12 72	86.81 121	91.33 183

xiu_mvs_v1_base_debi		82.16 110	81.12 113	85.26 110	86.42 225	68.72 63	92.59 128	90.44 201	73.12 148	84.20 60	94.36 80	38.04 301	95.73 143	84.12 72	86.81 121	91.33 183

F-COLMAP		70.66 262	68.44 267	77.32 281	86.37 228	55.91 310	88.00 260	86.32 299	56.94 324	57.28 313	88.07 191	33.58 323	92.49 259	51.02 291	68.37 250	83.55 292

CDS-MVSNet		81.43 121	80.74 119	83.52 158	86.26 229	64.45 177	92.09 143	90.65 196	75.83 100	73.95 163	89.81 171	63.97 86	92.91 242	71.27 172	82.82 153	93.20 142
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VDDNet		80.50 138	78.26 159	87.21 41	86.19 230	69.79 41	94.48 48	91.31 170	60.42 304	79.34 106	90.91 151	38.48 297	96.56 116	82.16 88	81.05 166	95.27 63

jason		86.40 43	86.17 44	87.11 44	86.16 231	70.54 27	95.71 24	92.19 136	82.00 23	84.58 56	94.34 85	61.86 110	95.53 159	87.76 41	90.89 90	95.27 63
jason: jason.
PCF-MVS	73.15 9	79.29 160	77.63 171	84.29 140	86.06 232	65.96 137	87.03 272	91.10 180	69.86 228	69.79 213	90.64 154	57.54 152	96.59 113	64.37 235	82.29 155	90.32 197
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		77.90 190	76.50 189	82.12 194	85.99 233	69.95 37	91.75 163	92.70 113	73.97 129	62.58 285	84.44 231	41.11 287	95.78 140	63.76 239	92.17 72	80.62 327

FIs		79.47 158	79.41 144	79.67 251	85.95 234	59.40 277	91.68 165	93.94 61	78.06 70	68.96 221	88.28 185	66.61 58	91.77 278	66.20 219	74.99 208	87.82 227

VPA-MVSNet		79.03 163	78.00 163	82.11 197	85.95 234	64.48 176	93.22 101	94.66 34	75.05 113	74.04 162	84.95 225	52.17 214	93.52 227	74.90 145	67.04 259	88.32 223

tpm		78.58 177	77.03 181	83.22 165	85.94 236	64.56 172	83.21 298	91.14 179	78.31 66	73.67 164	79.68 292	64.01 85	92.09 272	66.07 220	71.26 234	93.03 148

OpenMVS	70.45 11	78.54 178	75.92 197	86.41 72	85.93 237	71.68 16	92.74 117	92.51 123	66.49 260	64.56 266	91.96 137	43.88 278	98.10 39	54.61 280	90.65 93	89.44 209

OMC-MVS		78.67 176	77.91 167	80.95 226	85.76 238	57.40 301	88.49 254	88.67 268	73.85 132	72.43 180	92.10 135	49.29 240	94.55 189	72.73 157	77.89 187	90.91 192

miper_ehance_all_eth		77.60 192	76.44 190	81.09 223	85.70 239	64.41 181	90.65 205	88.64 270	72.31 168	67.37 246	82.52 249	64.77 78	92.64 255	70.67 177	65.30 267	86.24 256

KD-MVS_2432*1600		69.03 275	66.37 277	77.01 285	85.56 240	61.06 249	81.44 309	90.25 210	67.27 254	58.00 308	76.53 315	54.49 190	87.63 322	48.04 304	35.77 358	82.34 312

miper_refine_blended		69.03 275	66.37 277	77.01 285	85.56 240	61.06 249	81.44 309	90.25 210	67.27 254	58.00 308	76.53 315	54.49 190	87.63 322	48.04 304	35.77 358	82.34 312

EI-MVSNet		78.97 165	78.22 160	81.25 213	85.33 242	62.73 223	89.53 234	93.21 92	72.39 167	72.14 183	90.13 167	60.99 115	94.72 181	67.73 202	72.49 224	86.29 254

CVMVSNet		74.04 238	74.27 217	73.33 310	85.33 242	43.94 355	89.53 234	88.39 274	54.33 332	70.37 202	90.13 167	49.17 242	84.05 337	61.83 253	79.36 175	91.99 173

ACMH	63.93 17	68.62 278	64.81 286	80.03 241	85.22 244	63.25 208	87.72 265	84.66 317	60.83 302	51.57 330	79.43 295	27.29 343	94.96 173	41.76 330	64.84 274	81.88 317
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cl____		76.07 211	74.67 207	80.28 235	85.15 245	61.76 238	90.12 219	88.73 266	71.16 208	65.43 258	81.57 263	61.15 113	92.95 237	66.54 213	62.17 295	86.13 260

DIV-MVS_self_test		76.07 211	74.67 207	80.28 235	85.14 246	61.75 239	90.12 219	88.73 266	71.16 208	65.42 259	81.60 262	61.15 113	92.94 241	66.54 213	62.16 297	86.14 258

TAMVS		80.37 140	79.45 143	83.13 167	85.14 246	63.37 206	91.23 185	90.76 192	74.81 116	72.65 173	88.49 181	60.63 119	92.95 237	69.41 187	81.95 159	93.08 147

MSDG		69.54 271	65.73 280	80.96 225	85.11 248	63.71 199	84.19 287	83.28 330	56.95 323	54.50 318	84.03 233	31.50 331	96.03 132	42.87 327	69.13 245	83.14 302

c3_l		76.83 204	75.47 202	80.93 227	85.02 249	64.18 190	90.39 212	88.11 281	71.66 192	66.65 253	81.64 261	63.58 95	92.56 256	69.31 189	62.86 289	86.04 262

ACMP	71.68 10	75.58 225	74.23 218	79.62 253	84.97 250	59.64 273	90.80 201	89.07 256	70.39 221	62.95 281	87.30 202	38.28 298	93.87 220	72.89 153	71.45 232	85.36 277
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-test		77.99 186	78.08 162	77.70 274	84.89 251	55.51 313	90.27 215	93.75 70	76.87 88	66.80 252	87.59 197	65.71 67	90.23 299	62.89 246	73.94 213	87.37 234

PVSNet_0	68.08 15	71.81 257	68.32 269	82.27 186	84.68 252	62.31 229	88.68 251	90.31 207	75.84 99	57.93 310	80.65 280	37.85 304	94.19 202	69.94 182	29.05 363	90.31 198

eth_miper_zixun_eth		75.96 217	74.40 215	80.66 229	84.66 253	63.02 213	89.28 239	88.27 278	71.88 183	65.73 256	81.65 260	59.45 133	92.81 245	68.13 197	60.53 311	86.14 258

WR-MVS		76.76 205	75.74 200	79.82 247	84.60 254	62.27 230	92.60 126	92.51 123	76.06 97	67.87 238	85.34 220	56.76 163	90.24 298	62.20 250	63.69 286	86.94 242

ACMH+	65.35 16	67.65 287	64.55 289	76.96 287	84.59 255	57.10 304	88.08 259	80.79 336	58.59 317	53.00 324	81.09 275	26.63 345	92.95 237	46.51 311	61.69 304	80.82 324

VPNet		78.82 169	77.53 173	82.70 174	84.52 256	66.44 125	93.93 72	92.23 130	80.46 40	72.60 174	88.38 184	49.18 241	93.13 233	72.47 161	63.97 284	88.55 217

IterMVS-LS		76.49 207	75.18 205	80.43 232	84.49 257	62.74 222	90.64 206	88.80 264	72.40 166	65.16 261	81.72 259	60.98 116	92.27 268	67.74 201	64.65 278	86.29 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		78.15 184	77.55 172	79.98 242	84.46 258	60.26 265	92.25 136	93.20 94	77.50 82	68.88 222	86.61 207	66.10 61	92.13 270	66.38 216	62.55 291	87.54 229

FMVSNet5		68.04 284	65.66 282	75.18 298	84.43 259	57.89 291	83.54 291	86.26 301	61.83 298	53.64 323	73.30 329	37.15 311	85.08 333	48.99 299	61.77 300	82.56 311

MVS-HIRNet		60.25 317	55.55 323	74.35 303	84.37 260	56.57 307	71.64 340	74.11 350	34.44 360	45.54 349	42.24 362	31.11 334	89.81 303	40.36 337	76.10 205	76.67 348

LPG-MVS_test		75.82 220	74.58 211	79.56 255	84.31 261	59.37 278	90.44 209	89.73 231	69.49 231	64.86 262	88.42 182	38.65 295	94.30 197	72.56 159	72.76 221	85.01 281

LGP-MVS_train				79.56 255	84.31 261	59.37 278		89.73 231	69.49 231	64.86 262	88.42 182	38.65 295	94.30 197	72.56 159	72.76 221	85.01 281

ACMM	69.62 13	74.34 235	72.73 236	79.17 260	84.25 263	57.87 292	90.36 213	89.93 223	63.17 283	65.64 257	86.04 215	37.79 305	94.10 204	65.89 221	71.52 231	85.55 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		77.58 193	76.78 185	79.98 242	84.11 264	60.80 253	91.76 161	93.17 97	76.56 94	69.93 211	84.78 227	63.32 98	92.36 265	64.89 232	62.51 293	86.78 244

test_0402		64.54 302	61.09 308	74.92 299	84.10 265	60.75 257	87.95 261	79.71 340	52.03 336	52.41 326	77.20 309	32.21 329	91.64 280	23.14 362	61.03 307	72.36 354

LTVRE_ROB	59.60 19	66.27 295	63.54 296	74.45 302	84.00 266	51.55 330	67.08 351	83.53 326	58.78 315	54.94 317	80.31 284	34.54 321	93.23 232	40.64 336	68.03 252	78.58 343
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
miper_lstm_enhance		73.05 246	71.73 248	77.03 284	83.80 267	58.32 289	81.76 304	88.88 261	69.80 229	61.01 290	78.23 301	57.19 154	87.51 324	65.34 229	59.53 316	85.27 280

Patchmatch-test		65.86 297	60.94 309	80.62 230	83.75 268	58.83 283	58.91 358	75.26 349	44.50 355	50.95 334	77.09 311	58.81 142	87.90 318	35.13 349	64.03 282	95.12 70

nrg030		80.93 131	79.86 134	84.13 143	83.69 269	68.83 60	93.23 100	91.20 174	75.55 102	75.06 152	88.22 190	63.04 102	94.74 180	81.88 90	66.88 260	88.82 213

GA-MVS		78.33 182	76.23 193	84.65 129	83.65 270	66.30 129	91.44 171	90.14 215	76.01 98	70.32 203	84.02 234	42.50 282	94.72 181	70.98 173	77.00 200	92.94 151

FMVSNet1		72.71 253	69.91 260	81.10 220	83.60 271	65.11 161	90.01 223	90.32 204	63.92 276	63.56 276	80.25 286	36.35 316	91.54 283	54.46 281	66.75 261	86.64 246

OPM-MVS		79.00 164	78.09 161	81.73 202	83.52 272	63.83 194	91.64 167	90.30 208	76.36 96	71.97 186	89.93 170	46.30 266	95.17 169	75.10 139	77.70 189	86.19 257
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tfpnnormal		70.10 266	67.36 272	78.32 268	83.45 273	60.97 251	88.85 248	92.77 111	64.85 271	60.83 292	78.53 298	43.52 280	93.48 228	31.73 359	61.70 303	80.52 328

Effi-MVS+-dtu		76.14 210	75.28 204	78.72 265	83.22 274	55.17 315	89.87 227	87.78 286	75.42 104	67.98 234	81.43 265	45.08 272	92.52 258	75.08 140	71.63 229	88.48 218

mvs-test1		78.74 173	77.95 165	81.14 217	83.22 274	57.13 303	93.96 67	87.78 286	75.42 104	72.68 172	90.80 153	45.08 272	94.54 190	75.08 140	77.49 194	91.74 176

CR-MVSNet		73.79 242	70.82 254	82.70 174	83.15 276	67.96 81	70.25 341	84.00 323	73.67 139	69.97 209	72.41 332	57.82 149	89.48 306	52.99 288	73.13 218	90.64 195

RPMNet		70.42 265	65.68 281	84.63 131	83.15 276	67.96 81	70.25 341	90.45 198	46.83 351	69.97 209	65.10 349	56.48 170	95.30 167	35.79 348	73.13 218	90.64 195

DU-MVS		76.86 201	75.84 198	79.91 244	82.96 278	60.26 265	91.26 184	91.54 160	76.46 95	68.88 222	86.35 209	56.16 171	92.13 270	66.38 216	62.55 291	87.35 235

NR-MVSNet		76.05 214	74.59 210	80.44 231	82.96 278	62.18 231	90.83 200	91.73 152	77.12 86	60.96 291	86.35 209	59.28 137	91.80 277	60.74 257	61.34 306	87.35 235

XXY-MVS		77.94 188	76.44 190	82.43 180	82.60 280	64.44 178	92.01 148	91.83 149	73.59 140	70.00 208	85.82 216	54.43 193	94.76 178	69.63 184	68.02 253	88.10 226

TranMVSNet+NR-MVSNet		75.86 219	74.52 213	79.89 245	82.44 281	60.64 261	91.37 179	91.37 169	76.63 92	67.65 240	86.21 212	52.37 213	91.55 282	61.84 252	60.81 309	87.48 231

IterMVS		72.65 255	70.83 253	78.09 272	82.17 282	62.96 215	87.64 267	86.28 300	71.56 200	60.44 293	78.85 297	45.42 271	86.66 328	63.30 242	61.83 299	84.65 285
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		67.53 289	63.93 294	78.34 267	82.12 283	64.38 182	68.72 345	84.00 323	48.23 348	59.24 298	72.41 332	57.82 149	89.27 307	46.10 314	56.68 326	81.36 320

PatchT		69.11 274	65.37 285	80.32 233	82.07 284	63.68 201	67.96 350	87.62 288	50.86 340	69.37 214	65.18 348	57.09 155	88.53 313	41.59 332	66.60 262	88.74 214

MIMVSNet		71.64 258	68.44 267	81.23 214	81.97 285	64.44 178	73.05 338	88.80 264	69.67 230	64.59 265	74.79 325	32.79 325	87.82 319	53.99 283	76.35 204	91.42 181

MVP-Stereo		77.12 200	76.23 193	79.79 248	81.72 286	66.34 128	89.29 238	90.88 190	70.56 220	62.01 288	82.88 245	49.34 238	94.13 203	65.55 227	93.80 46	78.88 340
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IterMVS-SCA-FT		71.55 259	69.97 258	76.32 291	81.48 287	60.67 260	87.64 267	85.99 305	66.17 262	59.50 297	78.88 296	45.53 269	83.65 341	62.58 248	61.93 298	84.63 286

COLMAP_ROB	57.96 20	62.98 310	59.65 312	72.98 313	81.44 288	53.00 324	83.75 290	75.53 348	48.34 347	48.81 340	81.40 267	24.14 348	90.30 294	32.95 355	60.52 312	75.65 350
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
JIA-IIPM		66.06 296	62.45 303	76.88 288	81.42 289	54.45 319	57.49 359	88.67 268	49.36 344	63.86 273	46.86 358	56.06 174	90.25 295	49.53 297	68.83 246	85.95 265

WR-MVS_H		70.59 263	69.94 259	72.53 316	81.03 290	51.43 331	87.35 270	92.03 140	67.38 253	60.23 294	80.70 277	55.84 178	83.45 343	46.33 313	58.58 321	82.72 306

Fast-Effi-MVS+-dtu		75.04 230	73.37 230	80.07 240	80.86 291	59.52 276	91.20 188	85.38 309	71.90 181	65.20 260	84.84 226	41.46 286	92.97 236	66.50 215	72.96 220	87.73 228

Baseline_NR-MVSNet		73.99 239	72.83 234	77.48 278	80.78 292	59.29 280	91.79 158	84.55 318	68.85 240	68.99 220	80.70 277	56.16 171	92.04 273	62.67 247	60.98 308	81.11 321

CP-MVSNet		70.50 264	69.91 260	72.26 319	80.71 293	51.00 334	87.23 271	90.30 208	67.84 247	59.64 296	82.69 247	50.23 231	82.30 351	51.28 290	59.28 317	83.46 296

v8		75.35 226	73.26 231	81.61 206	80.67 294	66.82 115	89.54 233	89.27 244	71.65 193	63.30 279	80.30 285	54.99 186	94.06 208	67.33 206	62.33 294	83.94 289

PS-MVSNAJss		77.26 198	76.31 192	80.13 239	80.64 295	59.16 281	90.63 208	91.06 184	72.80 155	68.58 229	84.57 230	53.55 202	93.96 216	72.97 152	71.96 228	87.27 238

TransMVSNet (Re)		70.07 267	67.66 271	77.31 282	80.62 296	59.13 282	91.78 160	84.94 314	65.97 263	60.08 295	80.44 282	50.78 225	91.87 275	48.84 300	45.46 347	80.94 323

v2v482		77.42 195	75.65 201	82.73 173	80.38 297	67.13 106	91.85 156	90.23 212	75.09 112	69.37 214	83.39 242	53.79 200	94.44 194	71.77 168	65.00 273	86.63 249

MVS_0304		68.99 277	67.23 274	74.28 305	80.36 298	52.54 325	87.01 274	86.36 298	59.89 310	66.22 254	73.56 328	24.25 347	88.03 317	57.34 273	70.11 237	82.27 314

PS-CasMVS		69.86 270	69.13 264	72.07 322	80.35 299	50.57 336	87.02 273	89.75 228	67.27 254	59.19 300	82.28 251	46.58 261	82.24 352	50.69 292	59.02 318	83.39 298

v10		74.77 233	72.54 240	81.46 208	80.33 300	66.71 119	89.15 243	89.08 255	70.94 212	63.08 280	79.86 290	52.52 211	94.04 211	65.70 224	62.17 295	83.64 291

test0.0.03 1		72.76 251	72.71 237	72.88 314	80.25 301	47.99 346	91.22 186	89.45 238	71.51 202	62.51 286	87.66 196	53.83 198	85.06 334	50.16 294	67.84 256	85.58 272

v1144		76.73 206	74.88 206	82.27 186	80.23 302	66.60 122	91.68 165	90.21 214	73.69 137	69.06 219	81.89 256	52.73 210	94.40 195	69.21 190	65.23 270	85.80 268

v148		76.19 209	74.47 214	81.36 210	80.05 303	64.44 178	91.75 163	90.23 212	73.68 138	67.13 247	80.84 276	55.92 177	93.86 222	68.95 193	61.73 302	85.76 271

v1192		75.98 216	73.92 224	82.15 191	79.73 304	66.24 131	91.22 186	89.75 228	72.67 157	68.49 230	81.42 266	49.86 234	94.27 199	67.08 207	65.02 272	85.95 265

AllTest		61.66 312	58.06 316	72.46 317	79.57 305	51.42 332	80.17 319	68.61 359	51.25 338	45.88 345	81.23 269	19.86 357	86.58 329	38.98 340	57.01 324	79.39 336

TestCases				72.46 317	79.57 305	51.42 332		68.61 359	51.25 338	45.88 345	81.23 269	19.86 357	86.58 329	38.98 340	57.01 324	79.39 336

MDA-MVSNet-bldmvs		61.54 314	57.70 318	73.05 312	79.53 307	57.00 305	83.08 299	81.23 334	57.57 318	34.91 360	72.45 331	32.79 325	86.26 331	35.81 347	41.95 351	75.89 349

v144192		76.05 214	74.03 222	82.12 194	79.50 308	66.55 124	91.39 176	89.71 234	72.30 169	68.17 232	81.33 268	51.75 217	94.03 213	67.94 199	64.19 280	85.77 269

v1921920		75.63 224	73.49 229	82.06 198	79.38 309	66.35 127	91.07 194	89.48 237	71.98 178	67.99 233	81.22 271	49.16 243	93.90 219	66.56 212	64.56 279	85.92 267

PEN-MVS		69.46 272	68.56 266	72.17 321	79.27 310	49.71 340	86.90 275	89.24 245	67.24 257	59.08 301	82.51 250	47.23 258	83.54 342	48.42 302	57.12 322	83.25 299

v1240		75.21 229	72.98 233	81.88 200	79.20 311	66.00 135	90.75 203	89.11 254	71.63 197	67.41 244	81.22 271	47.36 257	93.87 220	65.46 228	64.72 277	85.77 269

pmmvs4		73.92 240	71.81 247	80.25 237	79.17 312	65.24 155	87.43 269	87.26 293	67.64 252	63.46 277	83.91 236	48.96 245	91.53 286	62.94 244	65.49 266	83.96 288

D2MVS		73.80 241	72.02 245	79.15 262	79.15 313	62.97 214	88.58 253	90.07 217	72.94 151	59.22 299	78.30 299	42.31 284	92.70 251	65.59 226	72.00 227	81.79 318

V42		76.46 208	74.55 212	82.19 190	79.14 314	67.82 85	90.26 216	89.42 240	73.75 135	68.63 228	81.89 256	51.31 221	94.09 205	71.69 170	64.84 274	84.66 284

pm-mvs1		72.89 249	71.09 252	78.26 270	79.10 315	57.62 296	90.80 201	89.30 243	67.66 250	62.91 282	81.78 258	49.11 244	92.95 237	60.29 261	58.89 319	84.22 287

our_test_3		68.29 282	64.69 288	79.11 263	78.92 316	64.85 170	88.40 257	85.06 312	60.32 306	52.68 325	76.12 319	40.81 288	89.80 305	44.25 322	55.65 327	82.67 310

ppachtmachnet_test		67.72 286	63.70 295	79.77 249	78.92 316	66.04 134	88.68 251	82.90 332	60.11 308	55.45 316	75.96 320	39.19 292	90.55 291	39.53 338	52.55 336	82.71 307

TinyColmap		60.32 316	56.42 322	72.00 323	78.78 318	53.18 323	78.36 329	75.64 346	52.30 335	41.59 356	75.82 322	14.76 362	88.35 314	35.84 346	54.71 332	74.46 351

SixPastTwentyTwo		64.92 300	61.78 307	74.34 304	78.74 319	49.76 339	83.42 295	79.51 341	62.86 285	50.27 335	77.35 306	30.92 335	90.49 293	45.89 315	47.06 345	82.78 303

EG-PatchMatch MVS		68.55 279	65.41 284	77.96 273	78.69 320	62.93 216	89.86 228	89.17 249	60.55 303	50.27 335	77.73 305	22.60 352	94.06 208	47.18 310	72.65 223	76.88 347

pmmvs5		73.35 244	71.52 249	78.86 264	78.64 321	60.61 262	91.08 192	86.90 294	67.69 249	63.32 278	83.64 237	44.33 276	90.53 292	62.04 251	66.02 265	85.46 275

UniMVSNet_ETH3D		72.74 252	70.53 255	79.36 257	78.62 322	56.64 306	85.01 283	89.20 247	63.77 278	64.84 264	84.44 231	34.05 322	91.86 276	63.94 237	70.89 236	89.57 207

XVG-OURS		74.25 237	72.46 241	79.63 252	78.45 323	57.59 297	80.33 316	87.39 290	63.86 277	68.76 226	89.62 173	40.50 289	91.72 279	69.00 192	74.25 210	89.58 206

XVG-OURS-SEG-HR		74.70 234	73.08 232	79.57 254	78.25 324	57.33 302	80.49 314	87.32 291	63.22 282	68.76 226	90.12 169	44.89 274	91.59 281	70.55 179	74.09 212	89.79 203

MDA-MVSNet_test_wron		63.78 307	60.16 310	74.64 300	78.15 325	60.41 263	83.49 292	84.03 321	56.17 329	39.17 358	71.59 338	37.22 309	83.24 346	42.87 327	48.73 342	80.26 331

YYNet1		63.76 308	60.14 311	74.62 301	78.06 326	60.19 268	83.46 294	83.99 325	56.18 328	39.25 357	71.56 339	37.18 310	83.34 344	42.90 326	48.70 343	80.32 330

DTE-MVSNet		68.46 281	67.33 273	71.87 324	77.94 327	49.00 343	86.16 280	88.58 272	66.36 261	58.19 305	82.21 253	46.36 262	83.87 340	44.97 320	55.17 329	82.73 305

USDC		67.43 291	64.51 290	76.19 292	77.94 327	55.29 314	78.38 328	85.00 313	73.17 146	48.36 341	80.37 283	21.23 354	92.48 260	52.15 289	64.02 283	80.81 325

jajsoiax		73.05 246	71.51 250	77.67 275	77.46 329	54.83 316	88.81 249	90.04 221	69.13 238	62.85 283	83.51 239	31.16 333	92.75 248	70.83 174	69.80 238	85.43 276

mvs_tets		72.71 253	71.11 251	77.52 276	77.41 330	54.52 318	88.45 256	89.76 227	68.76 243	62.70 284	83.26 243	29.49 337	92.71 249	70.51 180	69.62 240	85.34 278

N_pmnet		50.55 325	49.11 328	54.88 342	77.17 331	4.02 378	84.36 286	2.00 377	48.59 345	45.86 347	68.82 343	32.22 328	82.80 348	31.58 360	51.38 338	77.81 345

test_djsdf		73.76 243	72.56 239	77.39 280	77.00 332	53.93 320	89.07 245	90.69 193	65.80 264	63.92 272	82.03 255	43.14 281	92.67 252	72.83 154	68.53 249	85.57 273

bset_n11_16_dypcd		75.95 218	74.16 219	81.30 212	76.91 333	65.14 160	88.89 247	87.48 289	74.30 122	69.90 212	83.40 241	42.16 285	92.42 261	78.39 120	66.03 264	86.32 253

OpenMVS_ROB	61.12 18	66.39 294	62.92 300	76.80 289	76.51 334	57.77 293	89.22 240	83.41 328	55.48 330	53.86 322	77.84 304	26.28 346	93.95 217	34.90 350	68.76 247	78.68 342

v7n		71.31 260	68.65 265	79.28 258	76.40 335	60.77 255	86.71 277	89.45 238	64.17 275	58.77 304	78.24 300	44.59 275	93.54 226	57.76 270	61.75 301	83.52 294

K. test v3		63.09 309	59.61 313	73.53 309	76.26 336	49.38 342	83.27 296	77.15 343	64.35 273	47.77 343	72.32 334	28.73 339	87.79 320	49.93 296	36.69 357	83.41 297

RPSCF		64.24 304	61.98 306	71.01 326	76.10 337	45.00 352	75.83 335	75.94 345	46.94 350	58.96 302	84.59 229	31.40 332	82.00 353	47.76 308	60.33 315	86.04 262

OurMVSNet-221017-0		64.68 301	62.17 305	72.21 320	76.08 338	47.35 349	80.67 313	81.02 335	56.19 327	51.60 329	79.66 293	27.05 344	88.56 312	53.60 286	53.63 334	80.71 326

Anonymous20231206		67.53 289	65.78 279	72.79 315	74.95 339	47.59 348	88.23 258	87.32 291	61.75 299	58.07 307	77.29 308	37.79 305	87.29 326	42.91 325	63.71 285	83.48 295

ITE_SJBPF				70.43 327	74.44 340	47.06 350		77.32 342	60.16 307	54.04 321	83.53 238	23.30 351	84.01 338	43.07 324	61.58 305	80.21 333

EU-MVSNet		64.01 305	63.01 299	67.02 336	74.40 341	38.86 364	83.27 296	86.19 303	45.11 353	54.27 319	81.15 274	36.91 314	80.01 356	48.79 301	57.02 323	82.19 316

XVG-ACMP-BASELINE		68.04 284	65.53 283	75.56 295	74.06 342	52.37 326	78.43 327	85.88 306	62.03 294	58.91 303	81.21 273	20.38 356	91.15 289	60.69 258	68.18 251	83.16 301

CL-MVSNet_self_test		69.92 268	68.09 270	75.41 296	73.25 343	55.90 311	90.05 222	89.90 224	69.96 226	61.96 289	76.54 314	51.05 224	87.64 321	49.51 298	50.59 340	82.70 308

anonymousdsp		71.14 261	69.37 263	76.45 290	72.95 344	54.71 317	84.19 287	88.88 261	61.92 296	62.15 287	79.77 291	38.14 300	91.44 288	68.90 194	67.45 258	83.21 300

lessismore_v0				73.72 308	72.93 345	47.83 347		61.72 367		45.86 347	73.76 327	28.63 341	89.81 303	47.75 309	31.37 362	83.53 293

pmmvs6		67.57 288	64.76 287	76.00 294	72.82 346	53.37 322	88.71 250	86.78 297	53.19 334	57.58 312	78.03 303	35.33 319	92.41 262	55.56 278	54.88 331	82.21 315

testgi		64.48 303	62.87 301	69.31 329	71.24 347	40.62 359	85.49 281	79.92 339	65.36 268	54.18 320	83.49 240	23.74 350	84.55 335	41.60 331	60.79 310	82.77 304

Patchmatch-RL test		68.17 283	64.49 291	79.19 259	71.22 348	53.93 320	70.07 343	71.54 356	69.22 235	56.79 314	62.89 351	56.58 168	88.61 310	69.53 186	52.61 335	95.03 76

Gipuma		34.91 332	31.44 335	45.30 347	70.99 349	39.64 363	19.85 367	72.56 353	20.10 366	16.16 368	21.47 368	5.08 372	71.16 360	13.07 366	43.70 350	25.08 365
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		65.79 298	63.10 298	73.88 306	70.71 350	50.29 338	81.09 311	89.88 225	72.58 159	49.25 339	74.77 326	32.57 327	87.43 325	55.96 277	41.04 353	83.90 290

CMPMVS	48.56 21	66.77 293	64.41 292	73.84 307	70.65 351	50.31 337	77.79 332	85.73 308	45.54 352	44.76 350	82.14 254	35.40 318	90.14 301	63.18 243	74.54 209	81.07 322
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		63.83 306	62.65 302	67.38 335	70.58 352	39.94 360	86.57 278	84.17 320	63.29 281	51.86 328	77.30 307	37.09 312	82.47 349	38.87 342	54.13 333	79.73 334

MIMVSNet1		60.16 318	57.33 319	68.67 330	69.71 353	44.13 354	78.92 325	84.21 319	55.05 331	44.63 351	71.85 336	23.91 349	81.54 355	32.63 357	55.03 330	80.35 329

pmmvs-eth3d		65.53 299	62.32 304	75.19 297	69.39 354	59.59 274	82.80 301	83.43 327	62.52 290	51.30 332	72.49 330	32.86 324	87.16 327	55.32 279	50.73 339	78.83 341

UnsupCasMVSNet_bld		61.60 313	57.71 317	73.29 311	68.73 355	51.64 329	78.61 326	89.05 257	57.20 322	46.11 344	61.96 352	28.70 340	88.60 311	50.08 295	38.90 355	79.63 335

Anonymous20240521		62.09 311	59.08 314	71.10 325	67.19 356	48.72 344	83.91 289	85.23 311	50.38 341	47.84 342	71.22 340	20.74 355	85.51 332	46.47 312	58.75 320	79.06 339

KD-MVS_self_test		60.87 315	58.60 315	67.68 333	66.13 357	39.93 361	75.63 336	84.70 316	57.32 321	49.57 338	68.45 344	29.55 336	82.87 347	48.09 303	47.94 344	80.25 332

new-patchmatchnet		59.30 320	56.48 321	67.79 332	65.86 358	44.19 353	82.47 302	81.77 333	59.94 309	43.65 354	66.20 347	27.67 342	81.68 354	39.34 339	41.40 352	77.50 346

PM-MVS		59.40 319	56.59 320	67.84 331	63.63 359	41.86 356	76.76 333	63.22 365	59.01 314	51.07 333	72.27 335	11.72 364	83.25 345	61.34 254	50.28 341	78.39 344

DSMNet-mixed		56.78 321	54.44 324	63.79 338	63.21 360	29.44 368	64.43 353	64.10 364	42.12 357	51.32 331	71.60 337	31.76 330	75.04 358	36.23 345	65.20 271	86.87 243

new_pmnet		49.31 326	46.44 329	57.93 340	62.84 361	40.74 358	68.47 347	62.96 366	36.48 359	35.09 359	57.81 354	14.97 361	72.18 359	32.86 356	46.44 346	60.88 360

LF4IMVS		54.01 324	52.12 325	59.69 339	62.41 362	39.91 362	68.59 346	68.28 361	42.96 356	44.55 352	75.18 323	14.09 363	68.39 361	41.36 333	51.68 337	70.78 355

ambc				69.61 328	61.38 363	41.35 357	49.07 362	85.86 307		50.18 337	66.40 346	10.16 365	88.14 316	45.73 316	44.20 348	79.32 338

TDRefinement		55.28 323	51.58 326	66.39 337	59.53 364	46.15 351	76.23 334	72.80 352	44.60 354	42.49 355	76.28 318	15.29 360	82.39 350	33.20 354	43.75 349	70.62 356

pmmvs3		55.51 322	51.50 327	67.53 334	57.90 365	50.93 335	80.37 315	73.66 351	40.63 358	44.15 353	64.75 350	16.30 359	78.97 357	44.77 321	40.98 354	72.69 352

test_method		38.59 330	35.16 333	48.89 346	54.33 366	21.35 373	45.32 363	53.71 368	7.41 370	28.74 361	51.62 356	8.70 368	52.87 367	33.73 351	32.89 361	72.47 353

DeepMVS_CX				34.71 350	51.45 367	24.73 372		28.48 376	31.46 361	17.49 367	52.75 355	5.80 371	42.60 371	18.18 364	19.42 364	36.81 363

FPMVS		45.64 327	43.10 330	53.23 344	51.42 368	36.46 365	64.97 352	71.91 354	29.13 362	27.53 362	61.55 353	9.83 366	65.01 365	16.00 365	55.58 328	58.22 361

wuyk23d		11.30 339	10.95 342	12.33 354	48.05 369	19.89 374	25.89 366	1.92 378	3.58 371	3.12 373	1.37 372	0.64 377	15.77 373	6.23 371	7.77 371	1.35 369

PMMVS2		37.93 331	33.61 334	50.92 345	46.31 370	24.76 371	60.55 357	50.05 369	28.94 363	20.93 364	47.59 357	4.41 374	65.13 364	25.14 361	18.55 365	62.87 359

E-PMN		24.61 334	24.00 338	26.45 351	43.74 371	18.44 375	60.86 355	39.66 371	15.11 367	9.53 371	22.10 367	6.52 370	46.94 369	8.31 369	10.14 367	13.98 367

EMVS		23.76 336	23.20 340	25.46 352	41.52 372	16.90 376	60.56 356	38.79 374	14.62 368	8.99 372	20.24 370	7.35 369	45.82 370	7.25 370	9.46 368	13.64 368

LCM-MVSNet		40.54 328	35.79 331	54.76 343	36.92 373	30.81 367	51.41 360	69.02 358	22.07 364	24.63 363	45.37 360	4.56 373	65.81 363	33.67 352	34.50 360	67.67 357

ANet_high		40.27 329	35.20 332	55.47 341	34.74 374	34.47 366	63.84 354	71.56 355	48.42 346	18.80 366	41.08 363	9.52 367	64.45 366	20.18 363	8.66 370	67.49 358

MVE	24.84 23	24.35 335	19.77 341	38.09 349	34.56 375	26.92 370	26.57 365	38.87 373	11.73 369	11.37 370	27.44 365	1.37 376	50.42 368	11.41 367	14.60 366	36.93 362
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	26.43 22	31.84 333	28.16 336	42.89 348	25.87 376	27.58 369	50.92 361	49.78 370	21.37 365	14.17 369	40.81 364	2.01 375	66.62 362	9.61 368	38.88 356	34.49 364
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		22.26 337	23.75 339	17.80 353	5.23 377	12.06 377	35.26 364	39.48 372	2.82 372	18.94 365	44.20 361	22.23 353	24.64 372	36.30 344	9.31 369	16.69 366

testmvs		7.23 341	9.62 344	0.06 356	0.04 378	0.02 380	84.98 284	0.02 379	0.03 373	0.18 374	1.21 373	0.01 379	0.02 374	0.14 372	0.01 372	0.13 371

test123		6.92 342	9.21 345	0.08 355	0.03 379	0.05 379	81.65 307	0.01 380	0.02 374	0.14 375	0.85 374	0.03 378	0.02 374	0.12 373	0.00 373	0.16 370

test_blank		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 373	0.00 372

eth-test2					0.00 380

eth-test					0.00 380

uanet_test		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 373	0.00 372

cdsmvs_eth3d_5k		19.86 338	26.47 337	0.00 357	0.00 380	0.00 381	0.00 368	93.45 83	0.00 375	0.00 376	95.27 50	49.56 236	0.00 376	0.00 374	0.00 373	0.00 372

pcd_1.5k_mvsjas		4.46 343	5.95 346	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	53.55 202	0.00 376	0.00 374	0.00 373	0.00 372

sosnet-low-res		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 373	0.00 372

sosnet		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 373	0.00 372

uncertanet		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 373	0.00 372

Regformer		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 373	0.00 372

ab-mvs-re		7.91 340	10.55 343	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	94.95 62	0.00 380	0.00 376	0.00 374	0.00 373	0.00 372

uanet		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 373	0.00 372

PC_three_1452									80.91 36	94.07 2	96.83 14	83.57 4	99.12 5	95.70 2	97.42 4	97.55 4

test_241102_TWO								94.41 45	71.65 193	92.07 6	97.21 5	74.58 16	99.11 6	92.34 7	95.36 14	96.59 15

test_0728_THIRD									72.48 162	90.55 15	96.93 10	76.24 11	99.08 11	91.53 15	94.99 17	96.43 26

GSMVS																94.68 85

sam_mvs1												57.85 148				94.68 85

sam_mvs												54.91 187

MTGPA								92.23 130

test_post1							78.95 324				20.70 369	53.05 207	91.50 287	60.43 259

test_post											23.01 366	56.49 169	92.67 252

patchmatchnet-post											67.62 345	57.62 151	90.25 295

MTMP							93.77 82	32.52 375

test9_res														89.41 24	94.96 18	95.29 59

agg_prior2														86.41 55	94.75 30	95.33 55

test_prior4						67.18 105	93.92 73

test_prior2							95.10 37		75.40 106	85.25 53	95.61 40	67.94 43		87.47 45	94.77 25

旧先验2							92.00 150		59.37 313	87.54 28			93.47 229	75.39 137

新几何2							91.41 172

无先验							92.71 119	92.61 120	62.03 294				97.01 92	66.63 210		93.97 118

原ACMM2							92.01 148

testdata2													96.09 127	61.26 255

segment_acmp												65.94 63

testdata1							89.21 241		77.55 81

plane_prior5								91.31 170					95.55 157	76.74 129	78.53 184	88.39 221

plane_prior4											89.14 177

plane_prior3						61.95 235			79.09 56	72.53 176

plane_prior2							93.13 102		78.81 61

plane_prior						62.42 225	93.85 77		79.38 49						78.80 181

n2								0.00 381

nn								0.00 381

door-mid								66.01 363

test11								93.01 103

door								66.57 362

HQP5-MVS						63.66 202

BP-MVS														77.63 126

HQP4-MVS										74.18 157			95.61 151			88.63 215

HQP3-MVS								91.70 155							78.90 179

HQP2-MVS												51.63 219

MDTV_nov1_ep13_2view						59.90 271	80.13 320		67.65 251	72.79 171		54.33 195		59.83 263		92.58 159

ACMMP++_ref															71.63 229

ACMMP++															69.72 239

Test By Simon												54.21 196

High-resolution multi-view benchmark